Animals synthesize visual, acoustic, chemical, tactile and social information as they navigate their environment. The central nervous system integrates these stimuli with internal information and past experience in order to guide adaptive behavioral decisions (i.e. approach or avoidance of a salient stimulus). Stimulus processing depends on the state of local and distributed brain networks (Fontanini & Katz 2008). The network state is the emergent structure of ongoing activity in the brain: the response properties of one neural element (e.g. a single neuron, assembly of neurons or a brain region) is affected by the modulatory activity of the network it is embedded in (Bressler & McIntosh 2007). Neural context (i.e. network state) is a determinative factor in sensory processing, influencing not only the perception of stimuli but also behavioral decision-making (Goodson & Kabelik 2009). Across vertebrates, social behavior is linked to a core network of brain regions called the social decision making network (SDMN). The SDMN is comprised of 11 brain regions, many of which are bidirectionally connected to one another and are sensitive to sex steroid hormones (SSH). They have been linked to a large variety of social and sexual behaviors across vertebrates (O'Connell & Hofmann 2012; Crews 2003; Goodson 2005). My overarching hypothesis is that neural context in the SDM network represents an animals internal computing framework for interpreting external social information and that SSHs preconfigure the neural context of the network. Consistent with this hypothesis, I expect that ongoing neural activity will be influenced by SSHs and that this neuromodulatory patterning will be correlated to the neural responses evoked by social interaction. As a general approach I will exploit the different time courses of two neural activity measures, cytochrome oxidase (COX) and egr-1, within the same animals to measure ongoing neural activity and also activity evoked by social interactions.
These two measures combine to make a powerful experimental approach. Cytochrome oxidase is the final electron acceptor in the mitochonrdial electron transport chain and is thus an integral component in producing ATP, the main source of energy for nerve cells (Wong-Riley 1989). Wong-Riley (1979) introduced a histochemical procedure for measuring cytochrome oxidase and it has subsuqently been linked to neural activity in humans, rodents and lizards (Valla et al. 2001; Gonzalez-Lima & Garrosa 1991; Sakata et al. 2000). Egr-1 is a transcription factor that is activated in nerve cells in response to many different social stimuli and is part of a gene family called immediate early genes, which together regulate many of the neuronal responses to synaptic signals (Hofmann 2010). Egr-1 can be measured using radioactive in situ hybridization, a method which is compatible with COX histochemistry, such that the two can be done on adjacent sections of the same animal. COX is a longer term measure which changes over the course of days to weeks as an integrated measure of metabolic capacity of a particular brain area. Egr-1 on the other hand is activated within minuites to an hour after a stimulus and is therefore a short term marker of activity. To better understand the differences one can use the analogy of a mucsel like a bicep. If someone were to work out over many days the bicep would get larger, and one could create a model relating the amount of training to the size of the bicep as well as the bicep’s capacity to lift weight. However if you wanted to know how much the bicep was used in a specific exercise you would need to use a more real-time measurment of the muscel flexure. The first measure, the size of the bicep, is analogous to the measurement of cytochrome oxidase since it tells you the metabolic capacity of particular brain area but not the extent of its activity in any particular task. The second measure, muscel flexure, is analogous to the meausrment of egr-1 since it tells you more about the acitivy of a brain area in reponse to a specific stimulus (analogy parafrased from Gonzalez-Lima 1998).