Modulation of mGlu5 reduces rewarding associative properties of nicotine via changes in mesolimbic plasticity: Relevance to comorbid cigarette smoking in psychosis.

RATIONALE: Antipsychotic medications that are used to treat psychosis are often limited in their efficacy by high rates of severe side effects. Treatment success in schizophrenia is further complicated by high rates of comorbid nicotine use. Dopamine D2 heteroreceptor complexes have recently emerged as targets for the development of more efficacious pharmaceutical treatments for schizophrenia. OBJECTIVE: The current study sought to explore the use of the positive allosteric modulator of the mGlu5 receptor 3-Cyano-N-(1,3-diphenyl-1H-pyrazol-5-yl)benzamide (CDPPB) as a treatment to reduce symptoms related to psychosis and comorbid nicotine use. METHODS: Neonatal treatment of animals with the dopamine D2-like receptor agonist quinpirole (NQ) from postnatal day (P)1-21 produces a lifelong increase in D2 receptor sensitivity, showing relevance to psychosis and comorbid tobacco use disorder. Following an 8-day conditioning paradigm, brain tissue in the mesolimbic pathway was analyzed for several plasticity markers, including brain derived neurotrophic factor (BDNF), phosphorylated p70 ribosomal S6 kinase (phospho-p70S6K), and cadherin-13 (Cdh13). RESULTS: Pretreatment with CDPPB was effective to block enhanced nicotine conditioned place preference observed in NQ-treated animals. Pretreatment was additionally effective to block the nicotine-induced increase in BDNF and sex-dependent increases in cadherin-13 in the ventral tegmental area (VTA), as well as increased phospho-p70S6K in the nucleus accumbens (NAcc) shell found in NQ-treated animals. CONCLUSION: In conjunction with prior work, the current study suggests positive allosteric modulation of the mGlu5 receptor, an emerging target for schizophrenia therapeutics, may be effective for the treatment of comorbid nicotine abuse in psychosis.

Interval estimation for linear functions of medians in within-subjects and mixed designs.

The currently available distribution-free confidence interval for a difference of medians in a within-subjects design requires an unrealistic assumption of identical distribution shapes. A confidence interval for a general linear function of medians is proposed for within-subjects designs that do not assume identical distribution shapes. The proposed method can be combined with a method for linear functions of independent medians to provide a confidence interval for a linear function of medians in mixed designs. Simulation results show that the proposed methods have good small-sample properties under a wide range of conditions. The proposed methods are illustrated with examples, and R functions that implement the new methods are provided.

Varying coefficient meta-analysis methods for odds ratios and risk ratios.

Odds ratios and risk ratios are useful measures of effect size in 2-group studies in which the response variable is dichotomous. Confidence interval methods are proposed for combining and comparing odds ratios and risk ratios in multistudy designs. Unlike the traditional fixed-effect meta-analysis methods, the proposed varying coefficient methods do not require effect-size homogeneity, and unlike the random-effects meta-analysis methods, the proposed varying coefficient methods do not assume that the effect sizes from the selected studies represent a random sample from a normally distributed superpopulation of effect sizes. The results of extensive simulation studies suggest that the proposed varying coefficient methods have excellent performance characteristics under realistic conditions and should provide useful alternatives to the currently used meta-analysis methods. (PsycINFO Database Record

Meta-analysis methods for risk differences.

The difference between two proportions, referred to as a risk difference, is a useful measure of effect size in studies where the response variable is dichotomous. Confidence interval methods based on a varying coefficient model are proposed for combining and comparing risk differences from multi-study between-subjects or within-subjects designs. The proposed methods are new alternatives to the popular constant coefficient and random coefficient methods. The proposed varying coefficient methods do not require the constant coefficient assumption of effect size homogeneity, nor do they require the random coefficient assumption that the risk differences from the selected studies represent a random sample from a normally distributed superpopulation of risk differences. The proposed varying coefficient methods are shown to have excellent finite-sample performance characteristics under realistic conditions.

Confidence intervals for a ratio of two independent binomial proportions.

Several large-sample confidence intervals for the ratio of independent binomial proportions are compared in terms of exact coverage probability and width. A non-iterative approximate Bayesian interval is derived and its frequency properties are superior to all of the non-iterative confidence intervals considered. The approximate Bayesian interval, which is very easy to compute, has performance characteristics that are very similar to the computationally intensive score method. Two sample size determination formulas are presented, one for desired absolute precision and the other for desired relative precision.

TouchStat v. 3.00: a new and improved Monte Carlo adjunct for the sequential touching task.

The sequential-touching procedure is employed by researchers studying nonlinguistic categorization in toddlers. TouchStat 3.00 is introduced in this article as an adjunct to the sequential-touching procedure, allowing researchers to compare children's actual touching behavior to what might be expected by chance. Advantages over the Thomas and Dahlin (2000) framework include ease of use, and fewer assumptive limitations. Improvements over TouchStat 1.00 include calculation of chance probabilities for multiple "special cases" and for immediate intercategory alternations. A new feature for calculating mean run length is also included.

Confidence intervals for a ratio of binomial proportions based on paired data.

Four interval estimation methods for the ratio of marginal binomial proportions are compared in terms of expected interval width and exact coverage probability. Two new methods are proposed that are based on combining two Wilson score intervals. The new methods are easy to compute and perform as well or better than the method recently proposed by Nam and Blackwelder. Two sample size formulas are proposed to approximate the sample size required to achieve an interval estimate with desired confidence level and width.

Statistical inference for a linear function of medians: confidence intervals, hypothesis testing, and sample size requirements.

When the distribution of the response variable is skewed, the population median may be a more meaningful measure of centrality than the population mean, and when the population distribution of the response variable has heavy tails, the sample median may be a more efficient estimator of centrality than the sample mean. The authors propose a confidence interval for a general linear function of population medians. Linear functions have many important special cases including pairwise comparisons, main effects, interaction effects, simple main effects, curvature, and slope. The confidence interval can be used to test 2-sided directional hypotheses and finite interval hypotheses. Sample size formulas are given for both interval estimation and hypothesis testing problems.