Regulatory interactions of stress and reward on rat forebrain opioidergic and GABAergic circuitry

Palatable food intake reduces stress responses, suggesting that individuals may consume such ?comfort? food as self-medication for stress relief. The mechanism by which palatable foods provide stress relief is not known, but likely lies at the intersection of forebrain reward and stress regulatory circuits. Forebrain opioidergic and gamma-aminobutyric acid ergic signaling is critical for both reward and stress regulation, suggesting that these systems are prime candidates for mediating stress relief by palatable foods. Thus, the present study (1) determines how palatable ?comfort? food alters stress-induced changes in the mRNA expression of inhibitory neurotransmitters in reward and stress neurocircuitry and (2) identifies candidate brain regions that may underlie comfort food-mediated stress reduction. We used a model of palatable ?snacking? in combination with a model of chronic variable stress followed by in situ hybridization to determine forebrain levels of pro-opioid and glutamic acid decarboxylase (GAD) mRNA. The data identify regions within the extended amygdala, striatum, and hypothalamus as potential regions for mediating hypothalamic-pituitary-adrenal axis buffering following palatable snacking. Specifically, palatable snacking alone decreased pro-enkephalin-A (ENK) mRNA expression in the anterior bed nucleus of the stria terminalis (BST) and the nucleus accumbens, and decreased GAD65 mRNA in the posterior BST. Chronic stress alone increased ENK mRNA in the hypothalamus, nucleus accumbens, amygdala, and hippocampus; increased dynorphin mRNA in the nucleus accumbens; increased GAD65 mRNA in the anterior hypothalamus and BST; and decreased GAD65 mRNA in the dorsal hypothalamus. Importantly, palatable food intake prevented stress-induced gene expression changes in subregions of the hypothalamus, BST, and nucleus accumbens. Overall, these data suggest that complex interactions exist between brain reward and stress pathways and that palatable snacking can mitigate many of the neurochemical alterations induced by chronic stress.     

Using analysis of covariance (ANCOVA) with fallible covariates

Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but use ANCOVA anyway (and, most likely, report misleading results); (b) attempt to employ 1 of several measurement error models with the understanding that no research has examined their relative performance and with the added practical difficulty that several of these models are not available in commonly used statistical software; or (c) not use ANCOVA at all. First, we discuss analytic evidence to explain why using ANCOVA with fallible covariates produces bias and a systematic inflation of Type I error rates that may lead to the incorrect conclusion that treatment effects exist. Second, to provide a solution for this problem, we conduct 2 Monte Carlo studies to compare 4 existing approaches for adjusting treatment effects in the presence of covariate measurement error: errors-in-variables (EIV; Warren, White, & Fuller, 1974), Lord's (1960) method, Raaijmakers and Pieters's (1987) method (R&P), and structural equation modeling methods proposed by S?rbom (1978) and Hayduk (1996). Results show that EIV models are superior in terms of parameter accuracy, statistical power, and keeping Type I error close to the nominal value. Finally, we offer a program written in R that performs all needed computations for implementing EIV models so that ANCOVA can be used to obtain accurate results even when covariates are measured with error.