Developmental Changes in the Structure and Composition of the Postsynaptic Density

The development of the brain and its underlying circuitry is dependent on the formation of trillions of chemical synapses, which are highly specialized contacts that regulate the flow of information from one neuron to the next. It is through these synaptic connections that neurons wire together into networks capable of performing specific tasks, and activity-dependent changes in their structural and physiological state is one way that the brain is thought to adapt and store information. At the ultrastructural level, developmental and activity-dependent changes in the size and shape of dendritic spines have been well documented, and it is widely believed that structural changes in spines are a hallmark sign of synapse maturation and alteration of synaptic physiology. While changes in spine structure have been studied extensively, changes in one of its most prominent components, the postsynaptic density (PSD), have largely evaded observation. The PSD is a protein-rich organelle on the cytoplasmic side of the postsynaptic membrane, where it sits in direct opposition to the presynaptic terminal. The PSD functions both to cluster neurotransmitter receptors at the cell surface as well as organize the intracellular signaling molecules responsible for transducing extracellular signals to the postsynaptic cell. Much is known about the chemical composition of the PSD, but the structural arrangement of its molecular components is not well documented. Adding to the difficulty of understanding such a complex mass of protein machinery is the fact that its protein composition is known to change in response to synaptic activity, meaning that its structure is plastic and no two PSDs are identical. Here, immuno-gold labeling and electron tomography of PSDs isolated throughout development was used to track changes in both the structure and molecular composition of the PSD. State-of-the-art cryo-electron tomography was used to study the fine structure of the PSD during development, and provides an unprecedented glimpse into its molecular architecture in an un-fixed, unstained and hydrated state. Through this analysis, large structural and compositional changes are apparent and suggest a model by which the PSD is first assembled as a mesh-like lattice of proteins that function as support for the later recruitment of various PSD components. Spatial analysis of the recruitment of proteins into the PSD demonstrated that its assembly has an underlying order.

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^