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The inbred Roman low- (RLA-I) and high-avoidance (RHA-I) rats used in this study were initially selected and bred for extremely poor vs. rapid acquisition of active two-way avoidance behavior in the shuttle box. As a result of the selection for divergent avoidance acquisition, clear behavioral differences have been found between RHA and RLA rats in a variety of tasks related to anxiety and conflict. In rats of these two strains/lines previous brain studies have been performed, specifically in the striatum, the mesencephalic dopaminergic areas and the prefrontal cortex, as these brain areas are the classical ones for their critical role in sensitization and may play a role in the well-characterized anxiety response. In this study we analyzed, in RHA and RLA groups (N=5 each), the density of NeuN neurons counterstained with toluidine blue in the cingulate cortex (subdivision 1) and the hippocampus (CA1, CA2 and CA3). A statistical difference was found in the density of neurons of CA1 and CA2 (p=0.047 in both) and in the total density of the hippocampus (p=0.009). Contrary to our expectations, significant strain differences for the density of neurons in the cingulate cortex were not found. The relationship between those differences in the hippocampus and the between-strain differences in anxiety and in learning processes depending on anxiety are discussed.
Pubmed ID: 22698586
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NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything
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