Using Bayesian Networks to Investigate Psychological Constructs: The Case of Empathy.

Network analysis is an emerging field for the study of psychopathology that considers constructs as arising from the interactions among their constituents. Pairwise effects among psychological components are often investigated by using this framework. Few studies have applied Bayesian networks, models that include directed interactions to perform causal inference on psychological constructs. Directed graphical models may be less straightforward to interpret in case the construct at hand does not contain symptoms but instead psychometric items from self-report measures. However, they may be useful in validating specific research questions that arise while using standard pairwise network models. In this study, we use Bayesian networks to investigate a well-known psychological construct, empathy from the Interpersonal Reactivity Index, in large two samples of 1973 university students from Belgium. Overall, our results support the hypotheses emphasizing empathic concern (i.e., sympathy) as causally important in the construct of empathy, and overall attribute the primacy of emotional components of empathy over their intellectual counterparts. Bayesian networks help researchers identify the plausible causal relationships in psychometric data, to gain new insight on the psychological construct under examination, help generate new hypotheses and provide evidence relevant to old ones.

Bayesian Network Structure and Predictability of Autistic Traits.

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

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.

Network Structures of Symptoms From the Zung Depression Scale.

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. The network comparison test was performed to compare networks from female and male students. The network composed of items from the SDS is overall positively connected, although node connectivity varies. Item 11 ("My mind is as clear as it used to be") is the most interconnected item. Networks from female and male students did not differ. DAG reported directed edges among items. Network analysis is a useful tool to explore depression symptoms and offers new insight as to how they interact. Further studies may endeavor to replicate our findings in different samples, including clinical samples to replicate the network structures and determine possible viable targets for clinical intervention.

A Machine Learning Approach to Relationships Among Alexithymia Components.

BACKGROUND: The aim of this paper is to explore the network structures of alexithymia components and compare results with relevant prior literature. SUBJECTS AND METHODS: In a large sample of university students, undirected and directed network structures of items from the Bermond Vorst Alexithymia Questionnaire form B are estimated with state-of-the-art network analysis and structure learning tools. Centrality estimates are used to address the topic of item redundancy and select relevant alexithymia components to study. RESULTS: Alexithymia components present positive as well as negative connections; poor fantasy and emotional insight are identified as central items in the network. CONCLUSIONS: The undirected network structure of alexithymia components reports new features with respect to prior literature, and the directed network structures offers new insight on the construct.

Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders.

BACKGROUND: Sleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality. METHODS: Polysomnographic data from 44 healthy individuals (28 men and 16 women) recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realized by importing these connectivity matrices into EEGNET Toolbox. RESULTS: In men and women, all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep. However, compared to men, women present greater small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage, which indicates the presence of a small-world network organization less marked during REM sleep as well as for all EEG bands during this sleep stage in women. In addition, in women, these small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage are positively correlated with the presence of subclinical symptoms of depression. CONCLUSIONS: Thus, the highlighting of these sex differences in network organization during REM sleep indicates the presence of differences in the global and local processing of information during sleep between women and men. In addition, this small-world network organization less marked during REM sleep appears to be a marker of vulnerability to mood disorders specific to women, which opens up new perspectives in understanding sex differences in the occurrence of mood disorders.

Exploring network structure and central items of the Narcissistic Personality Inventory.

OBJECTIVES: The aim of this work is to explore the Narcissistic Personality Inventory (NPI) using network analysis in a dataset of 942 university students from the French-speaking part of Belgium. METHODS: We estimated an Ising Model for the forty items in the questionnaire and explored item interconnectedness with strength centrality. We provide in the supplementary materials the dataset used for the analyses as well as the full code to ensure the reproducibility of our results. RESULTS: The NPI is presented as an overall positively connected network with items from entitlement, authority and superiority reporting the highest centrality estimates. CONCLUSIONS: Network analysis highlights new properties of items from the NPI. Future studies should endeavor to replicate our findings in other samples, both clinical and non-clinical.

Network Approach to Items and Domains From the Toronto Alexithymia Scale.

The aim of this study is to explore network structures of the Toronto Alexithymia Scale in a large sample of 1925 French-speaking Belgian university students and compare results with previous studies from different samples and tools to identify potential targets for clinical intervention. We estimated network models for the 20 items of the Toronto Alexithymia Scale and for its three domains difficulty identifying feelings, difficulty describing feelings, and externally oriented thinking. We explored item connectivity through node predictability (shared variance with other network components). We performed an exploratory graph analysis to explore the dimensionality of our data set and compare results with the original three-factor model; because a different model was proposed, we estimated an additional network structure on the new structure. Items from the Toronto Alexithymia Scale connect both within and between domains. The three-domain network identifies difficulty describing feelings as the most connected domain. The exploratory graph analysis reported that three items from externally oriented thinking form a new domain, distraction. In the new four-domain network, difficulty describing feelings remains the most interconnected domain; however, two negative connections are found. Our findings support the relative importance of identifying and describing feelings as a meaningful target for intervention.

Alterations of neural network organisation during rapid eye movement sleep and slow-wave sleep in major depression: Implications for diagnosis, classification, and treatment.

The aim of this study was to empirically investigate the network organisation during rapid eye movement sleep (REMS) and slow-wave sleep (SWS) using the effective connectivity measured using the Granger causality to identify new potential biomarkers for the diagnosis, classification, and potential favourable response to treatment in major depression. Polysomnographic data were analysed from 24 healthy individuals and 16 major depressed individuals recruited prospectively. To obtain the 19×19 connectivity matrix of all possible pairwise combinations of electrodes by the Granger causality method from our electroencephalographic data, we used the Toolbox MVGC multivariate Granger causality. The computation of network measures was realised by importing these connectivity matrices into the EEGNET Toolbox. Major depressed individuals (versus healthy individuals) and those with endogenous depression (versus those with neurotic depression) present alterations of small-world network organisation during REMS, whereas major depressed individuals with potential favourable response to electroconvulsive therapy (versus those with potential unfavourable response) have a less efficient small-world network organisation during SWS. Thus, alterations in network organisation during REMS could be biomarkers for the diagnosis and classification of major depressive episodes, whereas alterations of network organisation during SWS could be a biomarker to predict potential favourable response to treatment by electroconvulsive therapy.

The sleep network organization during slow-wave sleep is more stable with age and has small-world characteristics more marked than during REM sleep in healthy men.

Sleep plays an important role in cognitive functioning. However, few studies have investigated the sleep network organization. The aim of our study was to empirically investigate the presence and the stability with age of a small-world network organization during REM and slow-wave sleep using the effective connectivity measured by the Granger causality. Polysomnographic data from 30 healthy men recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by the Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realised by importing these connectivity matrices into the EEGNET Toolbox. Even if all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep, slow-wave sleep seems to have a small-world network organization more marked than REM sleep. Moreover, the sleep network organization is affected greater by age during REM sleep than during slow-wave sleep. In healthy individuals, the highlighting of a sleep network organization during slow-wave sleep more stable with age and with small-world characteristics more marked than during REM sleep may help to better understand the global and local processing of information during sleep.

Prevalence and risk factors of excessive daytime sleepiness in major depression: A study with 703 individuals referred for polysomnography.

BACKGROUND: To date, few studies have investigated the prevalence and risk factors of excessive daytime sleepiness (EDS) in major depression. Thus, the aim of the present study was to examine the prevalence and risk factors of EDS in a large sample of individuals with major depression. METHODS: Data from 703 individuals with major depression were retrospectively collected from the sleep laboratory research database of Erasme Hospital for analysis. A score of > 10 on the Epworth Sleepiness Scale was used as the cut-off for EDS. Logistic regression analyses were conducted to examine the clinical and demographic risk factors of EDS in major depression. RESULTS: The prevalence of EDS in our sample was 50.8%. Multivariate logistic regression analysis revealed that the following were significant risk factors of EDS in major depression: non-use of short to intermediate half-life benzodiazepine receptor agonists, BMI ≥ 25 kg/m², age < 60 years, C-reactive protein > 7 mg/L, Beck Depression Inventory score ≥ 16, atypical depression, apnea-hypopnea index ≥ 15/h, and use of selective serotonin reuptake inhibitors or serotonin-norepinephrine reuptake inhibitors. LIMITATIONS: To evaluate EDS, we used the Epworth Sleepiness Scale, which only allows for a subjective measure of daytime sleepiness. CONCLUSION: EDS is a common symptom in individuals with major depression. In this subpopulation, interventions are possible for most risk factors of EDS, which justifies improved management of this symptom to avoid its negative consequences.

Objective sleep alterations and long-term use of short or intermediate half-life benzodiazepine receptor agonists are risk factors for high blood pressure in individuals with insomnia: a study in 1272 individuals referred for sleep examinations.

INTRODUCTION: Given conflicting data in the literature, the aim of this study was to examine the risk of high blood pressure (HBP) associated with sleep alterations, measured during polysomnography, and long-term use of benzodiazepine receptor agonists in a large sample of individuals with insomnia. METHODS: Demographic and polysomnographic data from 1272 individuals with insomnia recruited from the research database of the sleep laboratory of Erasme Hospital were analyzed. HBP status was defined by the presence of one of the following: self-report at interview of either a physician's diagnosis or taking antihypertensive medication; or an average systolic blood pressure ≥140 mm Hg or an average diastolic blood pressure ≥90 mm Hg at the medical examination. Logistic regression analyses were conducted to examine the risk of HBP associated with objective sleep alterations and long-term use of benzodiazepine receptor agonists in individuals with insomnia. RESULTS: The prevalence of HBP in individuals with insomnia is 30.03%. After adjustment for major confounding factors associated with HBP, multivariate logistic regression analysis revealed that short sleep duration (<5 h), severely reduced sleep efficiency (<65%), high sleep fragmentation (sleep fragmentation index ≥18/h), and long-term use of short or intermediate half-life benzodiazepine receptor agonists were significant risk factors for HBP in individuals with insomnia. CONCLUSION: In individuals with insomnia, objective sleep alterations and long-term use of short or intermediate half-life benzodiazepine receptor agonists are associated with higher risk of HBP. Therefore, better management of these reversible risk factors is required to avoid the negative consequences of the co-occurrence of insomnia and HBP.

Risk of high blood pressure associated with objective insomnia and self-reported insomnia complaints in major depression: A study on 703 individuals.

BACKGROUND: Since few studies have investigated the risk of high blood pressure associated with objective insomnia and self-reported insomnia complaints in major depression, the aim of this study was to examine this risk in a large sample of individuals with major depression. METHODS: Data from 703 individuals with major depression recruited from the research database of the sleep laboratory of the Erasme Hospital were analysed. High blood pressure status was defined by the presence of one of the following: self-reports at interview of either a physician-diagnosis or taking antihypertensive medication; or an average systolic blood pressure ≥140 mmHg or an average diastolic blood pressure ≥90 mmHg during at least two medical examinations. Logistic regression analyses were conducted to examine the risk of high blood pressure associated with objective insomnia and self-reported insomnia complaints in major depression. RESULTS: After adjustment for major confounding factors associated with high blood pressure, multivariate logistic regression analysis revealed that severe objective insomnia, low complaints of repeated nighttime awakenings or early morning awakening, and intermediate or low self-reported insomnia complaints were significant risk factors of high blood pressure in major depression. CONCLUSION: In major depression, severe objective insomnia and lower self-reported insomnia complaints are associated with higher risk of high blood pressure, which justifies a better management of objective insomnia and a better assessment of insomnia complaints in this particular subpopulation to avoid the negative consequences related to the co-occurrence of high blood pressure and major depression. Abbreviations: AHI, Apnea-Hypopnea Index; BDI, Beck Depression Inventory; BMI, Body Mass Index; DSM IV-TR, Diagnostic and Statistical Manual of Mental Disorders fourth edition - Text Revision; ESS, Epworth Sleepiness scale; ISI, Insomnia Severity Index; HBP, High Blood Pressure; OSA, Obstructive Sleep Apnea Syndrome; REM, rapid eye movement sleep.

Network analysis of Contingencies of Self-Worth Scale in 680 university students.

This study investigates the Contingencies of Self-Worth Scale (CSWS) in a sample of 680 university students from a network perspective. We estimated regularized partial correlations among seven CSWS domains: family support, competition, appearance, God's love, academic competence, virtue and other's approval. Competition - academic competence and competition - appearance represent the strongest connections in the network. Mean node predictability (shared variance with surrounding nodes) is 0.25. Appearance and academic competence were the most central (i.e., interconnected) domains in the network. Future studies should explore the network structure of self-worth in other healthy adult samples, and also in people with psychopathology. We provide the anonymized dataset as well as the full code in the supplementary materials to ensure complete reproducibility of the results.

Prevalence and risk factors of type 2 diabetes in insomnia sufferers: a study on 1311 individuals referred for sleep examinations.

BACKGROUND: Several studies have investigated the particular relationship between insomnia and type 2 diabetes. However, few studies have investigated the prevalence and risk factors for type 2 diabetes in insomnia sufferers. The aim of this study was to examine the prevalence and risk factors of type 2 diabetes in a large sample of insomnia sufferers. METHODS: Data from 1311 insomnia sufferers recruited from the research database of the Erasme Hospital sleep laboratory were analyzed. Only individuals with a diagnosis of type 2 diabetes according to the diagnostic criteria of the American Diabetes Association at admission were included in the diabetes group. Logistic regression analyses were conducted to examine clinical and demographic risk factors of type 2 diabetes in insomnia sufferers. RESULTS: The prevalence of type 2 diabetes in insomnia sufferers is 21.13%. Multivariate logistic regression analysis revealed that significant risk factors of type 2 diabetes in insomnia sufferers were as follows: being male, Z-drugs use, high blood pressure, hypertriglyceridemia, alcohol consumption of ≥4 units/day, BMI ≥25 & <30 kg/m2, BMI ≥30 kg/m2, age ≥50 years, C-reactive protein ≥4.5 mg/L, a sleep duration of <6.5 h, apnea-hypopnea index ≥15/hour, periodic limb movements index ≥26/hour, and severe complaints of early morning awakening. CONCLUSION: Type 2 diabetes is a common pathology in insomnia sufferers. In this subpopulation, most of the risk factors for type 2 diabetes are reversible, which justifies better prevention and management of this pathology in order to avoid its negative consequences.

Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults.

The aim of this work is to perform a network analysis on the French adaptation of the interpersonal reactivity index (IRI) scale from a large Belgian database and provide additional information for the construct of empathy. We analyze a database of 1973 healthy young adults who were queried on the IRI scale. A regularized partial correlation network is estimated. In the visualization of the model, items are displayed as nodes, edges represent regularized partial correlations between the nodes. Centrality denotes a node's connectedness with other nodes in the network. The spinglass algorithm and the walktrap algorithm are used to identify communities of items, and state-of-the-art stability analyses are carried out. The spinglass algorithm identifies four communities, the walktrap algorithm five communities. Positive edges are found among nodes belonging to the same community as well as among nodes belonging to different communities. Item 14 ("Other people's misfortunes do not usually disturb me a great deal") shows the highest strength centrality score. The network edges and node centrality order are accurately estimated. Network analysis highlights interesting connections between indicators of empathy; how these results impact empathy models must be assessed in further studies.

The dichotomy between low frequency and delta waves in human sleep: A reappraisal.

BACKGROUND: We present the results of an analysis of the low frequency (LF) (0.25-1Hz) and delta (1-4Hz) waves during human sleep. Our objective was to investigate whether LF and delta waves should be considered as separate entities. METHODS: The slow-wave electroencephalogram (EEG) activity of 2 sets of 10 young, healthy volunteers was analysed utilising the rarely-used Lomb-Scargle periodogram. This method has advantages over the more commonly-used Fast Fourier Transform analysis. RESULTS: During the night, the frequencies of the most powerful waves are concentrated in the 0.5-2Hz range and show a continuous tendency to shift towards slower frequencies during sleep. COMPARISON WITH EXISTING METHODS: When considering the frequency dynamics of slow-wave activity below 3Hz, the unifying theory of LF and delta waves is more parsimonious than the idea that there is a different origin and regulation for the two sub-bands. CONCLUSIONS: The unifying theory of LF and delta waves is the simplest explanation for the slow-wave activity of the EEG below 3Hz. This finding is important for the clinical use of slow-wave activity.

Prevalence and Risk Factors of Type 2 Diabetes in Major Depression: A Study on 703 Individuals Referred for Sleep Examinations.

BACKGROUND: Several studies have investigated the prevalence and risk factors of depression in individuals with type 2 diabetes. However, few studies have investigated the prevalence and risk factors for type 2 diabetes in major depression. OBJECTIVE: The aim of this study was to examine the prevalence and risk factors of type 2 diabetes in a large sample of individuals with major depression. METHODS: Data from 703 individuals with major depression recruited from the research database of the sleep laboratory of the Erasme Hospital were analysed. Only individuals with a diagnosis of type 2 diabetes according to the diagnostic criteria of the American Diabetes Association were included in the diabetes group. Logistic regression analyses were conducted to examine clinical and demographic risk factors of type 2 diabetes in major depression. RESULTS: The prevalence of type 2 diabetes in major depression is 21.2%. Multivariate logistic regression analysis revealed that male sex, high blood pressure, hypertriglyceridemia, BMI ≥30kg/m², age ≥50 years, sleep duration <6.5 hours, C-reactive protein ≥4.5mg/L, Beck Depression Inventory >12, and apnea-hypopnea index ≥5/h were significant risk factors of type 2 diabetes in major depression. CONCLUSION: Type 2 diabetes is a common condition in major depression. In this subpopulation, most of the risk factors for type 2 diabetes are reversible, which justifies better prevention and management of this disorder to avoid its negative consequences.

Prevalence and risk factors of moderate to severe obstructive sleep apnea syndrome in major depression: a observational and retrospective study on 703 subjects.

BACKGROUND: Several studies have investigated the prevalence and risk factors of depression in subjects with obstructive sleep apnea syndrome. However, few studies have investigated the prevalence and risk factors for obstructive sleep apnea syndrome in major depression. The aim of this study was to examine the prevalence and risk factors of moderate to severe obstructive sleep apnea syndrome in a large sample of individuals with major depression. METHODS: Data from 703 individuals with major depression recruited from the research database of the sleep laboratory of the Erasme Hospital were analysed. An apnea-hypopnea index of ≥15 events per hour was used as cut-off score for moderate to severe obstructive sleep apnea syndrome. Logistic regression analyses were conducted to examine clinical and demographic risk factors of moderate to severe obstructive sleep apnea syndrome in major depression. RESULTS: The prevalence of moderate to severe obstructive sleep apnea syndrome in major depression is 13.94%. Multivariate logistic regression analysis revealed that male gender, snoring, excessive daytime sleepiness, lower insomnia complaint, presence of metabolic syndrome, age ≥ 50 years, BMI >30 kg/m2, ferritin >300 µg/L, CRP >7 mg/L and duration of sleep ≥8 h were significant risk factors of moderate to severe obstructive sleep apnea syndrome in major depression. CONCLUSION: Moderate to severe obstructive sleep apnea syndrome is a common pathology in major depression. The identification of these different risk factors advances a new perspective for more effective screening of moderate to severe obstructive sleep apnea syndrome in major depression.

Prevalence and risk factors of excessive daytime sleepiness in insomnia sufferers: A study with 1311 individuals.

BACKGROUND: Several studies have investigated the prevalence and risk factors of excessive daytime sleepiness in the general population. However, few studies have investigated these in the particular subpopulation of insomnia sufferers. Thus, the aim of this study was to examine the prevalence and risk factors of excessive daytime sleepiness in a large sample of insomnia sufferers. METHODS: Data from 1311 insomnia sufferers with age≥18years and recruited from the research database of the sleep laboratory of the Erasme Hospital were analysed. A score>10 on the Epworth scale was used as the cut-off score for excessive daytime sleepiness. Logistic regression analyses were conducted to examine clinical and demographic risk factors of excessive daytime sleepiness in insomnia sufferers. RESULTS: The prevalence of excessive daytime sleepiness in our sample was 45.61%. Multivariate logistic regression analysis revealed that non-use of Z-drugs, non-use of Trazodone alone or in combination, body mass index≥25 & <30kg/m2, body mass index≥30kg/m2, age≥18 & <40years, age≥40 & <65years, Beck depression inventory score≥5 & <16, Beck depression inventory score≥16, apnea-hypopnea index≥15/h, and use of selective serotonin reuptake inhibitors were significant risk factors of excessive daytime sleepiness in the subpopulation of insomnia sufferers. CONCLUSION: Excessive daytime sleepiness is a common complaint for individuals with insomnia. In this subpopulation, most of the risk factors for excessive daytime sleepiness are reversible, which justifies better management of this complaint to avoid its negative consequences.