Guillermo Vallejo

Changes in brain oxidative metabolism induced by water maze training.

Although the hippocampus has been shown to be essential for spatial memory, the contribution of associated brain regions is not well established. Wistar rats were trained to find a hidden escape platform in the water maze during eight days. Following training, the oxidative metabolism in different brain regions was evaluated using cytochrome oxidase histochemistry. Metabolic activations were found in the prelimbic cortex, cornu ammonis (CA) 1 subfield of the dorsal hippocampus and the anterior thalamic nuclei, relative to yoked swim controls and naïve rats. In addition, many cross-correlations in brain metabolism were observed among the latter regions. These results support the implication of a hippocampal-prefrontal-thalamic system to spatial memory in rats.

Modified Brown-Forsythe Procedure for Testing Interaction Effects in Split-Plot Designs.

The standard univariate and multivariate methods are conventionally used to analyze continuous data from groups by trials repeated measures designs, in spite of being extremely sensitive to departures from the multisample sphericity assumption when group sizes are unequal. However, in the last 10 years several authors have offered alternative solutions to these tests that do not rest on this assumption. In an attempt to improve the precision of the Brown–Forsythe (BF) procedure, a new approximate degrees of freedom (df) approach is presented in this article. Unlike the BF test, the new method not only assures that the df will be always positive but also provides invariant solutions under linear transformations of the data. Monte Carlo methods are used to compare the new solution, in terms of control of Type I error rates, with the modified empirical generalized least squares and BF methods. Our extensive numerical studies show that the modified BF procedure outperformed the other two methods for a wide range of conditions.

Teachers' Feedback on Homework, Homework-Related Behaviors, and Academic Achievement.

ABSTRACT The authors intended to (a) identify the association between gender or grade level and teachers’ homework (HW) feedback and (b) examine the relationship between teachers’ HW feedback, HW-related behaviors (e.g., amount of HW completed), and academic achievement. Four hundred fifty-four students (Grades 5–12) participated in this study. The results showed that (a) at higher grade levels, there is a lower perceived amount of teachers’ HW feedback; (b) teachers’ HW feedback as perceived by students is positively and significantly related to the amount of HW completed and to the perceived quality of HW time management but not to the amount of time spent on HW; (c) the amount of HW completed and the perceived quality of HW time management positively and significantly predict academic achievement; and (d) teachers’ HW feedback as perceived by students has an indirect relationship with students’ academic achievement by its effect on students’ HW-related behaviors.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs

Empirical Type I error and power rates were estimated for (a) the doubly multivariate model, (b) the Welch-James multivariate solution developed by Keselman, Carriere and Lix (1993) using Johansens results (1980), and for (c) the multivariate version of the modified Brown-Forsythe (1974) procedure. The performance of these procedures was investigated by testing within- blocks sources of variation in a multivariate split-plot design containing unequal covariance matrices. The results indicate that the doubly multivariate model did not provide effective Type I error control while the Welch-James procedure provided robust and powerful tests of the within-subjects main effect, however, this approach provided liberal tests of the interaction effect. The results also indicate that the modified Brown-Forsythe procedure provided robust tests of within-subjects main and interaction effects, especially when the design was balanced or when group sizes and covariance matrices were positively paired.

Analyzing Small Samples of Repeated Measures Data with the Mixed-Model Adjusted F Test

This research examines the Type I error rates obtained when using the mixed model with the Kenward-Roger correction and compares them with the Between–Within and Satterthwaite approaches in split-plot designs. A simulated study was conducted to generate repeated measures data with small samples under normal distribution conditions. The data were obtained via three covariance matrices (unstructured, heterogeneous first-order auto-regressive, and random coefficients), the one with the best fit being selected according to the Akaike criterion. The results of the simulation study showed the Kenward-Roger test to be more robust, particularly when the population covariance matrices were unstructured or heterogeneous first-order auto-regressive. The main contribution of this study lies in showing that the Kenward–Roger method corrects the liberal Type I error rates obtained with the Between–Within and Satterthwaite approaches, especially with positive pairings between group sizes and covariance matrices.

Involvement of the mammillary bodies in spatial working memory revealed by cytochrome oxidase activity

In view of the inconclusive findings relating the nuclei of the mammillary bodies (MB) with spatial memory, we evaluated the oxidative metabolic activity of the medial and lateral nuclei of the mammillary bodies (MB) after training young rats (30 days) of both sexes in the Morris water maze. Different groups were trained in spatial working (WM) or reference memory (RM) tasks, respectively. The corresponding naïve groups swam for the same amount of time as the trained groups but without the escape platform. Control groups were added that had not been manipulated in any way. No sex-related differences were detected in the working memory task although males exhibited better reference memory than females. Cytochrome oxidase (CO) activity, an endogenous metabolic marker for neuronal activity, was measured in all the groups. CO activity increased significantly in both MB nuclei of male and female rats only in the spatial working memory group. In addition, high CO activity in the lateral nucleus of the MB was linearly correlated with lower escape latencies in both sexes after training in the working memory task. No CO activity changes were found in the basolateral amygdala (BL) in any of the experimental groups. This nucleus was used as a control brain region because of its participation in emotional behavior. The results suggest a specific role of the MB nuclei in spatial working memory in both sexes.

Comparison of Two Procedures for Analyzing Small Sets of Repeated Measures Data.

This article compares two methods for analyzing small sets of repeated measures data under normal and non-normal heteroscedastic conditions: a mixed model approach with the Kenward-Roger correction and a multivariate extension of the modified Brown-Forsythe (BF) test. These procedures differ in their assumptions about the covariance structure of the data and in the method of estimation of the parameters defining the mean structure. Simulation results show that the BF test outperformed its competitor, in terms of Type I errors, particularly when the total sample size was small, and the data were normally distributed. Under non-normal distributions the BF test tended to err on the side of conservatism. Results also indicate that neither method was uniformly more powerful. With very few exceptions, the power differences between these two methods depended on the population covariance structure, the nature of the pairing of covariance matrices and group sizes, and the relationship between mean vectors and dispersion matrices.

Consequences of Misspecifying the Error Covariance Structure in Linear Mixed Models for Longitudinal Data

Abstract. Repeated measures and longitudinal data are frequently analyzed using a linear mixed model. According to this approach, rather than presuming a certain type of covariance structure analysts choose the model that best describes their data prior to carrying out inferences of interest. Because it is not possible to know the underlying covariance structure in advance, researchers often use fit criteria to select from possible covariance structures. SAS Institutes (2004) Proc Mixed program, allows users to model the correct covariance structure by comparing Akaikes Information Criterion (AIC), Hurvich and Tsais Criterion (AICC), Schwarzs Bayesian Criterion (BIC), Bozdogans Criterion (CAIC), and Hannan and Quinns Criterion (HQIC). Monte Carlo methods are used to examine performance of these criteria. The program also investigated the effects of misspecification on properties of the inferences. The results of the simulation show that neither criterion always lead to correct selection of model and...

Evidence for sexual difference in astrocytes of adult rat hippocampus

We quantified the number of glial fibrillary acidic protein immunoreactive (GFAP-IR) astrocytes in the CA1 and CA3 areas of the adult rat hippocampus. The dorsal and ventral regions of the hippocampus were taken into account to estimate the GFAP-IR cells using unbiased stereological techniques. Males had a higher number of GFAP-IR astrocytes in the CA3 area, whereas females had more in the CA1 area. No sex difference was found between dorsal and ventral regions, although most GFAP-IR astrocytes were located in the dorsal hippocampus.

Analysis of unbalanced factorial designs with heteroscedastic data

The present study investigates the operating characteristics of several Box-type and Welch–James (WJ) modifications on factorial designs lacking homogeneity, normality, and orthogonality. For comparison purposes the behaviours of Proc Mixed and Proc GLM, available from the SAS program, were also examined. When the shape of the distribution was symmetric, the Box-type, WJ, and Proc Mixed approaches consistently controlled the rates of error; however, when the distribution was moderately skewed only the Box-type approach limited the number of errors to the nominal value. In distributions with extreme skewness, the procedure was predominantly conservative but showed improved rates of Type-I error control using the Box–Cox method of power transformation. The execution of Proc GLM was considerably influenced by the presence of heterogeneity and scarcely affected by the absence of normality. With regard to test sensitivity, the WJ and Proc Mixed approaches were substantially more powerful than the Box-type approach when variances and cells sizes were negatively paired. However, they were equally powerful when this relationship was positive. When the population variances were homogeneous, the differences in power slightly favoured the Proc GLM approach.