To evaluate the impaired attention selection (Stroop interference effect) and general performance [reaction times (RTs)] on the Stroop task among methadone maintenance treatment (MMT) patients with obsessive compulsive disorder (OCD), pathological gambling (PG), both PG/OCD or none, and the influence if having ADHD.

Eighty-six patients and 15 control subjects underwent the Stroop task, which measured RTs of condition-related words (color, obsessive compulsive disorder, pathological gambling, addiction) and neutral words.

MMT patients had longer RTs on the Stroop task compared with controls. RTs were longer among patients with OCD and in those who abused drugs on the study day. The combined PG/OCD group had the longest RTs, but they were also characterized as abusing more drugs, being older, and having worse cognitive status. Stroop color interference differed only among MMT patients with ADHD, and it was higher among those with OCD than those without OCD. The modified condition-related Stroop did not show any interference effect of OCD, addiction, or gambling words.

MMT patients had generally poorer performance, as indicated by longer RTs, that were related to clinical OCD, drug abuse, poor cognitive state, and older age. Patients with both clinical OCD and ADHD had a higher Stroop interference effect, which is a reflection of an attention deficit. In order to improve clinical approach and treatment of MMT patients, OCD and ADHD should be evaluated (and treated as needed).

Resolving the entangled nosological dilemma of whether obsessive-compulsive disorder (OCD) with and without schizophrenia (schizo-OCD and OCD, respectively) are two independent entities or whether schizo-OCD is a combined product of its parent disorders.

Studying motor activity in OCD and in schizo-OCD patients. Performance of the patients was compared with the performance of the same motor task by a matching control individual.

Behavior in both schizo-OCD and OCD patients differed from controls in the excessive repetition and addition of acts, thus validating an identical OC facet. However, there was a significant difference in spatial behavior. Schizo-OCD patients traveled over a greater area with less focused activity as typical to schizophrenia patients and in contrast to OCD patients, who were more focused and traveled less in a confined area. While schizo-OCD and OCD patients share most of the OC ritualistic attributes, they differ in the greater spread of activity in schizo-OCD, which is related to schizophrenia disorder.

It is suggested that the finding on difference in spatial behavior is a reflection of the mental differences between OCD and schizophrenia. In other words, this could be an overt and observable manifestation of the mental state, and therefore may facilitate the nosology of OC spectrum disorders and OCD.

It seems as if both the OCD patients’ focus on specific thoughts, and the contrasting wandering thoughts of schizophrenia patients, are reflected in the focused activity of the former and wandering from one place to the next of the latter.

Differences in psychiatric background and dose–response to asenapine in patients with schizophrenia were examined based on efficacy and safety, using data obtained in a double-blind, placebo-controlled trial.

Patients with schizophrenia were classified into three clusters by a cluster analysis based on the Positive and Negative Symptom Scale (PANSS) subscores at baseline, using the data from a 6-week, double-blind, placebo-controlled trial. PANSS Marder factor scores were calculated for each cluster. The efficacy of 10 or 20 mg/day of asenapine on PANSS score was used as the primary endpoint, with the incidence of adverse events evaluated as the secondary endpoint.

A total of 529 asenapine-treated patients were classified into 3 clusters: Cluster-P with the higher scores in positive symptoms, disorganized thoughts, and hostility/excitement, Cluster-N with higher scores in negative symptoms, and Cluster-L with overall lower scores. In Cluster-N and Cluster-L, both 10 and 20 mg/day groups showed significant improvement in PANSS scores, while only the 20 mg/day group showed a significant difference in Cluster-P. Cluster-N and Cluster-L had differences in the incidence of adverse events, but this was not seen in Cluster-P.

The efficacy and safety of asenapine 10 and 20 mg/day differed between the 3 clusters of patients. This suggests that background information regarding baseline psychiatric symptoms may affect the therapeutic response in patients with schizophrenia.

To assess the efficacy of lamotrigine combined with either divalproex or lithium for the treatment of bipolar disorder.

Lithium and divalproex seem to be predominantly effective for manic and mixed symptoms of bipolar disorder, whereas lamotrigine may be more effective for bipolar depression than for mania. Combination therapy may provide more efficacious treatment for many patients with bipolar disorder.

Data from charts of adult outpatients with bipolar disorder treated with lamotrigine plus divalproex or lithium during a 3-year period were retrospectively analyzed. The Clinical Global Impressions for Bipolar Disorder scale was used to assess severity of illness at baseline (adjunct-therapy initiation) and improvement after 3 months of treatment. The safety and tolerability of the medication combinations were assessed.

After 3 months of treatment, 26 of 39 patients (67%) receiving lamotrigine plus divalproex had a depression rating of 1 (very much improved) or 2 (much improved) compared with seven of 16 (44%) taking lamotrigine plus lithium. The mania rating was 1 or 2 for 13 of 39 (39%) patients treated with lamotrigine plus divalproex and 7 of 16 (44%) patients receiving lamotrigine plus lithium. For overall illness severity, 26 of 39 (67%) patients given lamotrigine plus divalproex had scores of 1 or 2 compared with 10 of 16 (62%) patients taking lamotrigine plus lithium. Five of 39 patients (13%) taking lamotrigine plus divalproex and 5 of 16 (31%) receiving lamotrigine plus lithium discontinued at least one part of the combination in <3 months due to adverse events.

Combination therapy with lamotrigine plus divalproex or lithium may be a valuable option for managing symptoms of bipolar disorder. The combinations were generally well tolerated and apparently effective in improving depression as well as mania.The percentage of patients showing improvement in depression was somewhat larger. Tolerability was somewhat better for lamotrigine plus divalproex in combination than for lamotrigine plus lithium. Differences in tolerability are consistent with studies indicating poorer tolerability of lithium compared with divalproex.

Despite the prevalence of antidepressant-related sexual side effects, comparisons of treatments for these problematic side effects are lacking.

To address this, we performed a systematic review and Bayesian network meta-analysis to compare interventions for antidepressant-induced sexual dysfunction in adults. Using PubMed and clinicaltrials.gov, we identified published and unpublished prospective treatment trials from 1985 to September 2020 (primary outcome: the Arizona sexual experience scale [ASEX] score). The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation framework.

We identified 57 citations (27 randomized controlled trials, 66 treatment arms, 27 open-label trials, and 3 crossover trials) that evaluated 33 interventions (3108 patients). In the systematic review, 44% (25/57) of trials reported successful interventions; this was more common in open-label (70%, 19/27) compared to placebo-controlled studies (22%, 6/27). In the meta-analysis of placebo-controlled studies that used the ASEX (N = 8), pycnogenol was superior to placebo (standardized mean difference: −1.8, 95% credible interval [CrI]: [−3.7 to 0.0]) and there was evidence that, at a 6% threshold, sildenafil improved sexual dysfunction (standardized mean difference: −1.2, 95% CrI [−2.5 to 0.1]). In the meta-analysis including single-arm studies (15 studies), treatment response was more common with sildenafil, tianeptine, maca, tiagabine, and mirtazapine compared to placebo, but these differences failed to reach statistical significance.

While heterogeneity across randomized controlled trials complicates identifying the single best intervention, multiple trials suggest that sildenafil ameliorates antidepressant-induced sexual dysfunction. More randomized controlled trials are needed to examine the putative efficacy of other interventions.

A blunted response of the hypothalamic-pituitary-adrenal axis immediately after exposure to traumatic events has been proposed as a risk factor for posttraumatic stress disorder (PTSD). Accordingly, administration of hydrocortisone in the aftermath of a traumatic event is indicated. This study consisted of a randomized, placebo-controlled, double-blind trial investigating whether a single intravenous dose of hydrocortisone administered within 6 hours after exposure to trauma would reduce the incidence of PTSD at the 13-month follow-up.

A total of 118 consented patients with acute stress symptoms were administered a single intravenous bolus of hydrocortisone/placebo within 6 hours of the traumatic event. Blood samples were taken before hydrocortisone administration.

At 13 months, the hydrocortisone group did not differ from the placebo group regarding PTSD prevalence or symptom severity. However, a significant interaction between time of the trauma (ie, night, when cortisol’s level is low) and treatment was found. Specifically, a lower prevalence of PTSD was found at the 13-month follow-up in the hydrocortisone night group.

Administration of hydrocortisone within 6 hours of the traumatic event was not effective in preventing PTSD compared to placebo. However, nocturnal administration (when cortisol levels are low) may suggest a new venue for research.

The development of social science is inseparable from the development of research methods. The traditional research methods such as factor analysis, regression analysis, and classical measurement theory cannot meet the professional development of modern disciplines. Therefore, a new generation of statistical analysis methods came into being, among which the most prominent development is the development and application of structural equation. Structural equation model is a statistical method that represents the theory with a corresponding linear equation system on the basis of the existing theory. It makes up for the shortcomings of traditional statistical methods, deals with the relationship between multiple causes and results, and measures variables that cannot be directly observed. Structural equation model has become an important tool in multivariate data analysis, which is widely used in social science data analysis, especially in the fields of education and psychology.

As a research method based on statistical analysis technology, structural equation model can be used to deal with the exploration and analysis of complex multivariable research data, estimation of latent variables, parameter estimation, and fitting degree estimation of complex independent variables and dependent variables prediction model at the same time. The complete structural equation model includes two parts: measurement model and structural model. The measurement model describes the relationship between latent variables and actual measured variables, while the structural model describes the relationship between latent variables.Figure 1.

Diagram of the full structural equation model

As shown in Figure 1, a complete structural equation model, the lambda said the relationship between the observed variables and latent variables, gamma said the relationship between exogenous variables and endogenous potential, beta said the relationship between endogenous latent variable, the delta said exogenous variables to explain the latent variables incomplete measuring residual, epsilon says endogenous variable to explain the latent variables measuring residual incomplete; ζ represents the estimated residuals of endogenous latent variables that cannot be fully explained. Figure 1 shows the relationship between exogenous variables on the left and endogenous variables on the right. the complete structural model is composed of γ and β parameters. The relationship between various variables and parameters involved in Figure 1 can be described by a general linear equation:

The relationship between specific variables and specific parameters in Figure 1 can be described by the following two equations:

From the above overview, it is not difficult to find that if there is no assumption of latent variables in a structural equation model, only the measured variables, then it is no different from the traditional path analysis model. Therefore, in general, the structural equation model can not only deal with the estimation of multiple regression equations at the same time, but also make the processing of variable relations more elastic. Anyhow, structural equation modeling following the general linear model of multivariate analysis to verify model analysis, can put the variance analysis, regression analysis, path analysis, factor analysis of traditional statistical methods, such as included in the model, the structural equation modeling and investigation and experiment analysis can be used, therefore more extensive applicability.

Although structural equation model has obvious advantages over traditional statistical methods, it still has limitations, and it is conditional in psychological research.

First, different theoretical assumptions, if the same variables, can be used to construct multiple models. But the more variables you have, the more models you can combine. This will bring difficulty to the test of model fit. Therefore, the use of structural equation model should pay more attention to theoretical discussion, and should be based on the exact theory or reasonable logical reasoning.

Second, in psychological research, if only data are used to explore the causal relationship between latent variables without special design, no statistical method, including structural equation model, can prove whether there is a causal relationship between variables. Therefore, in order to model causality between variables using structural equations, it is necessary to control the design of longitudinal structural equation patterns to help determine causality.

Third, the introduction of latent variables into structural equation model is originally an advantage of structural equation model, which can solve the shortcomings of univariate measurement in the past. However, whether such measurement truly reflects latent variables or whether indicators can truly represent latent variables is a problem worthy of attention.

Fourth, structural equation modeling analysis of demand is a linear relationship between variables, but in many cases, this requirement is difficult to guarantee, and even sure is a linear relationship between variables, the researchers could not put all the variables and the relationship between the built into the theoretical construction, so the data fitting is its support for the theoretical construction, but it can’t prove the correctness of the theoretical construction.

The analysis process of structural equation model generally includes the following steps:

Step 1: Model construction

The model construction includes specifying: The relationship between observed variables and latent variables; The relationship between the latent variables; In complex models, the value or relationship of parameters such as factor loading or factor correlation coefficient can be limited.

Step 2: Model identification

Identifying the specified model is an important stage in building the structural equation model. Model identification focuses on whether a unique estimate can be obtained from the observed data. If there is a non-identifiable parameter in the model, the model is a non-identifiable model, and the model cannot be estimated. In order to make the model recognizable, it is usually necessary to check two necessary conditions: first, the degree of freedom cannot be negative; The second is that every latent variable in the model must have a measurement scale

Step 3: Model fitting

With a new model, it is required to find the solution of the model, the main is the estimation of the model parameters, this process is model fitting. If the fitting index reaches the standard, the model is well fitted.

Step 4: Model evaluation

If each parameter in the structural equation model is successfully estimated, the overall model can be evaluated. The evaluation of the model is to analyze the fitting of the hypothesized model with the actual observed data through different statistical procedures or the calculation of the fitting index. If the fitting degree of the model is not ideal, it means that the proposed hypothesis model has problems. At this time, it is necessary to adjust the parameter estimation content of the hypothesis model and fit it again until the model fit degree reaches the ideal level.

Structural equation models, as a major development in statistical methods, are being used more and more widely by researchers in the application of psychological research. the following are some common structural equation models.

Confirmatory factor analysis (CFA) models:

(1) The basic concept of confirmatory factor

Confirmatory factor analysis is used to test whether the factor structure defined by the hypothesis or theory can fit the corresponding data. In short, it is to test the hypothesized relationship between measured variables and latent variables, which is the most basic measurement part of structural equation modeling. The theoretical factor structure is considered valid if the hypothesized confirmatory factor analysis model fits the data.

(2) Examples of confirmatory factor analysis

In a structural equation model, it is confirmatory factor analysis if only the test of the measurement model is involved. We take the measurement of subjective well-being as an example to perform confirmatory factor analysis. The SWB measurement scale contains three dimensions, each dimension has three questions, a total of 9 questions. The hypothetical model of the measurement model is shown in Figure 2.Figure 2.

Hypothetical model of the confirmatory factor analysis model

Confirmatory factor analysis is to verify the degree of fit of the hypothesized model. After the analysis using relevant software, the path coefficient is used to measure the degree of influence or effect size between variables. If the fitting indices such as GFI value, RMSEA value and NFI value meet the required fit criteria, the model has a good fit and the quality of the questions is good. If the fit index did not meet the requirements, the model was modified and re-analyzed.

Path analysis:

(1) Basic concepts of path analysis

Path analysis is a technique for analyzing causal models. The main feature of path analysis is that it can estimate all the parameters of the model at the same time by taking advantage of the covariation of the measured variables, and test the fit between the theoretical model and the observed data in conjunction with the proposed specific hypothesis model to find the best model.

(2) Examples of path analysis

Some researchers have pointed out that path analysis is a complex model formed by a series of mediating effects. For example, there are many factors affecting SWB. We take attachment style as the influencing factor X and SWB as the dependent variable Y. There is at least one mediating variable M (loneliness) in the relationship between X and Y, which forms the mediating effect of X M Y. That is, the independent variable X influences the mediating variable M and then the dependent variable Y. As shown in Figure 3.Figure 3.

Mediating effect diagram

Integrated analysis model:

(1) The basic concept of integrated analysis model

Integrated model analysis refers to structural equation model analysis with both measurement model and structural model. Simply speaking, integrated model analysis can be considered as the synthesis of confirmatory factor analysis and path analysis, which takes into account both internal and external aspects and integrates the core concepts of both.

(2) Examples of integrated model analysis

Usually, a unified model analysis contains multiple latent variables, which can be used not only as exogenous and endogenous variables, but also as mediating variables. In the illustrative illustration of path analysis, we propose that the dependent variable X influences the independent variable Y by influencing the mediator variable M. In other words, the level of loneliness will affect the level of subjective well-being, but the level of loneliness will be affected by attachment style. As shown in Figure 4.Figure 4.

Figure of the integrated model of structural equations for individual subjective well-being

After assuming the model, model definition, parameter estimation and analysis should be carried out. After analysis, if all the fitting indices meet the fit criteria, then the model is good. If one item does not meet the fit criteria, the model still needs to be modified.

The wide application of structural equation model reflects the progress of data analysis and statistical methods. This new method of data analysis and statistics lays a methodological foundation for our comprehensive understanding and in-depth study of psychological phenomena. However, we should not blindly use this method, should fully grasp the basic principles of the constitutive equation model, applicable conditions to use this method, in order to give full play to the advantages of this method.