Place learning in scopolamine-treated rats: the roles of distal cues and catecholaminergic mediation

Experiments 1 and 2 tested the hypothesis that cholinergic receptor antagonists impair place learning in a water maze by interfering with the processing of distal, visual cues. Extramaze cues were offered to rats in the form of geometrical patterns arranged on the inner circumference of a curtain surrounding the water maze. In Experiment 1 the animals were offered both the distal cues and proximal cues in the form of pingpong balls in fixed positions on the surface of the water while only distal cues were present in Experiment 2. Animals were injected with either scopolamine (0.5 mg/kg body wt) or saline 20 min prior to the daily place learning sessions. Upon reaching criterion level performance the animals were tested on "rotation" sessions on which the distal cues were displaced. The outcome of such "rotations" demonstrated that-regardless of the presence or absence of proximal cues-scopolamine-treated rats relied at least as much as normal animals on the distal cues. The acquisition phase of both Experiments 1 and 2 demonstrated an almost complete lack of scopolamine-associated impairment in acquisition and performance of the place learning task. In Experiment 3 (when scopolamine was no longer administered) the subjects of Experiment 2 were exposed to a series of pharmacological "challenges" of their place learning performance and eventually to surgical ablation of the anteromedial prefrontal cortex. The outcome of the pharmacological challenges and the postoperative test of task performance demonstrated that the place learning performance of animals which had acquired the task under scopolamine was mediated by a neural substrate dissimilar to the substrate of task performance in normal animals. Rats acquiring the task while deprived of the cholinergic system demonstrated above-normal contributions to task mediation from catecholaminergic-probably dopaminergic-mechanisms and tentative results pointed to a "shift" toward prefrontal task mediation.     

Distinguishing Between Latent Classes and Continuous Factors with Categorical Outcomes: Class Invariance of Parameters of Factor Mixture Models

Factor mixture models are latent variable models with categorical and continuous latent variables that can be used as a model-based approach to clustering. A previous article covered the results of a simulation study showing that in the absence of model violations, it is usually possible to choose the correct model when fitting a series of models with different numbers of classes and factors within class. The response format in the first study was limited to normally distributed outcomes. This article has 2 main goals, first, to replicate parts of the first study with 5-point Likert scale and binary outcomes, and second, to address the issue of testing class invariance of thresholds and loadings. Testing for class invariance of parameters is important in the context of measurement invariance and when using mixture models to approximate nonnormal distributions. Results show that it is possible to discriminate between latent class models and factor models even if responses are categorical. Comparing models with and without class-specific parameters can lead to incorrectly accepting parameter invariance if the compared models differ substantially with respect to the number of estimated parameters. The simulation study is complemented with an illustration of a factor mixture analysis of 10 binary depression items obtained from a female subsample of the Virginia Twin Registry.     

Investigating population heterogeneity with factor mixture models

Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A step-by-step analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point.     

Cognitive development in 7- to 24-month-old extremely/very-to-moderately/late preterm and full-term born infants: The mediating role of focused attention

The present study analyzed the links between prematurity, attention, and global cognitive performance in infancy and early childhood. At 7 months, focused attention (FA) was examined with an object examination task in 93 preterm infants (39 of them born extremely/very preterm, 54 born moderately/late preterm, and 38 infants born full-term). Global cognition was assessed at 7 and 24 months with the Bayley-II cognitive scale. Groups did not differ with respect to global cognitive performance but FA of infants born extremely/very preterm was significantly lower than in infants born moderately/late preterm. FA correlated significantly with both prematurity and cognitive performance at 7 months of age but not with global cognition in childhood. Findings point to a subtle adverse effect of prematurity on early attention and reveal evidence for the mediating role of FA on the effect of prematurity on cognition.     

Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data

Growth mixture models (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent class variable. A full Bayesian method is then proposed to estimate the model. Through the data augmentation method, conditional posterior distributions for all model parameters and missing data are obtained. A Gibbs sampling procedure is then used to generate Markov chains of model parameters for statistical inference. The application of the model and the method is first demonstrated through the analysis of mathematical ability growth data from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, U.S. Department of Labor, 1997). A simulation study considering 3 main factors (the sample size, the class probability, and the missing data mechanism) is then conducted and the results show that the proposed Bayesian estimation approach performs very well under the studied conditions. Finally, some implications of this study, including the misspecified missingness mechanism, the sample size, the sensitivity of the model, the number of latent classes, the model comparison, and the future directions of the approach, are discussed.     

Comparison of different strategies to decrease negative affect and increase positive affect in women with borderline personality disorder

Background

In this study we compared the effect of different emotion regulation strategies on positive and negative emotions in patients with borderline personality disorder.

Methods

Emotion regulation strategies were a distracting task, individual positive memory imagery, individual soothing imagery, and a neutral comparison condition. During two separate sessions, 17 participants watched either neutral or negative movie segments before using these strategies.

Results

All three strategies influenced emotions into a favorable direction as compared to the neutral comparison condition. The positive memory image increased positive emotions significantly stronger than counting colors and distracting.

Discussion

Different strategies seem to have similar effects in decreasing negative emotions. Positive emotions may be affected in particular by positive and soothing imagery techniques.     

Schematherapie

Schema therapy is a cognitive behavioral therapy (CBT) development mainly for the treatment of personality disorders and other chronic mental disorders. It is characterized by an integration of cognitive, emotional and behavioral intervention methods derived from different therapeutic approaches with an emphasis on a specifically supportive therapeutic relationship. The original approach focused mainly on early maladaptive schemas. Current developments, however, concentrate on the concept of schema modes, describing different schema-associated emotional states. The schema mode approach is also used for specific case concepts for personality disorders. Effectiveness of schema therapy has been shown for borderline personality disorder. Current studies investigate the effectiveness of schema therapy for patients with other chronic mental conditions and as a group therapy approach. This paper provides an overview about case conceptualization and treatment, presents main research findings and discusses open questions and problems.