Impulsivity and Stress Response in Pathological Gamblers During the Trier Social Stress Test.

Gambling has been associated with increased sympathetic nervous system output and stimulation of the hypothalamic-pituitary-adrenal axis. However it is unclear how these systems are affected in pathological gambling. This study aimed to investigate the effect of the Trier Social Stress Test (TSST) on cortisol and on cardiac interbeat intervals in relation to impulsivity, in a sample of male pathological gamblers compared to healthy controls. In addition, we investigated the correlation between the TSST, duration of the disorder and impulsivity. A total of 35 pathological gamblers and 30 healthy controls, ranging from 19 to 58 years old and all male, participated in this study. Stress response was measured during and after the TSST by salivary cortisol and cardiac interbeat intervals; impulsivity was assessed with the Barratt Impulsiveness Scale (BIS-11). Exposure to the TSST produced a significant increase in salivary cortisol and interbeat intervals in both groups, without differences between groups. We found a negative correlation between baseline cortisol and duration of pathological gambling indicating that the longer the duration of the disorder the lower the baseline cortisol levels. Additionally, we found a main effect of impulsivity across groups on interbeat interval during the TSST, indicating an association between impulsivity and the intensity of the neurovegetative stress response during the TSST. Involvement of the hypothalamic-pituitary-adrenal axis in pathological gambling was confirmed together with evidence of a correlation between length of the disorder and diminished baseline cortisol levels. Impulsivity emerged as a personality trait expressed by pathological gamblers; however the neurovegetative response to the TSST, although associated with impulsivity, appeared to be independent of the presence of pathological gambling.

Modeling alcohol use disorder as a set of interconnected symptoms - Assessing differences between clinical and population samples and across external factors.

Alcohol use disorder is argued to be a highly complex disorder influenced by a multitude of factors on different levels. Common research approaches fail to capture this breadth of interconnecting symptoms. To address this gap in theoretical assumptions and methodological approaches, we used a network analysis to assess the interplay of alcohol use disorder symptoms. We applied the exploratory analysis to two US-datasets, a population sample with 23,591 individuals and a clinical sample with 483 individuals seeking treatment for alcohol use disorder. Using a Bayesian framework, we first investigated differences between the clinical and population sample looking at the symptom interactions and underlying structure space. In the population sample the time spent drinking alcohol was most strongly connected, whereas in the clinical sample loss of control showed most connections. Furthermore, the clinical sample demonstrated less connections, however, estimates were too unstable to conclude the sparsity of the network. Second, for the population sample we assessed whether the network was measurement invariant across external factors like age, gender, ethnicity and income. The network differed across all factors, especially for age subgroups, indicating that subgroup specific networks should be considered when deriving implications for theory building or intervention planning. Our findings corroborate known theories of alcohol use disorder stating loss of control as a central symptom in alcohol dependent individuals.

A Note on the Structural Change Test in Highly Parameterized Psychometric Models.

Equal parameter estimates across subgroups is a substantial requirement of statistical tests. Ignoring subgroup differences poses a threat to study replicability, model specification, and theory development. Structural change tests are a powerful statistical technique to assess parameter invariance. A core element of those tests is the empirical fluctuation process. In the case of parameter invariance, the fluctuation process asymptotically follows a Brownian bridge. This asymptotic assumption further provides the basis for inference. However, the empirical fluctuation process does not follow a Brownian bridge in small samples, and this situation is amplified in large psychometric models. Therefore, common methods of obtaining the sampling distribution are invalid and the structural change test becomes conservative. We discuss an alternative solution to obtaining the sampling distribution-permutation approaches. Permutation approaches estimate the sampling distribution through resampling of the dataset, avoiding distributional assumptions. Hereby, the tests power are improved. We conclude that the permutation alternative is superior to standard asymptotic approximations of the sampling distribution.