5-HT and GABA modulate intrinsic excitability of type I interneurons in Hermissenda.

The sensory neurons (photoreceptors) in the visual system of Hermissenda are one site of plasticity produced by Pavlovian conditioning. A second site of plasticity produced by conditioning is the type I interneurons in the cerebropleural ganglia. Both photoreceptors and statocyst hair cells of the graviceptive system form monosynaptic connections with identified type I interneurons. Two proposed neurotransmitters in the graviceptive system, serotonin (5-HT) and gamma-aminobutyric acid (GABA), have been shown to modify synaptic strength and intrinsic neuronal excitability in identified photoreceptors. However, the potential role of 5-HT and GABA in plasticity of type I interneurons has not been investigated. Here we show that 5-HT increased the peak amplitude of light-evoked complex excitatory postsynaptic potentials (EPSPs), enhanced intrinsic excitability, and increased spike activity of identified type I(e(A)) interneurons. In contrast, 5-HT decreased spike activity and intrinsic excitability of type I(e(B)) interneurons. The classification of two categories of type I(e) interneurons was also supported by the observation that 5-HT produced opposite effects on whole cell steady-state outward currents in type I(e) interneurons. Serotonin produced a reduction in the amplitude of light-evoked complex inhibitory PSPs (IPSPs), increased spontaneous spike activity, decreased intrinsic excitability, and depolarized the resting membrane potential of identified type I(i) interneurons. In contrast to the effects of 5-HT, GABA produced inhibition in both types of I(e) interneurons and type I(i) interneurons. These results show that 5-HT and GABA can modulate the intrinsic excitability of type I interneurons independent of the presynaptic effects of the same transmitters on excitability and synaptic efficacy of photoreceptors.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^