Network analysis of contextual fear conditioning learning in the absence of hippocampus

Abstract

The hippocampus is a central region that process information about events and the contexts in which they occur, as is important for these memory consolidation and retrieval. Humans and other animals with hippocampi lesion have a profoundly impaired performance in tasks requiring context, spatial or navigation information and alike. in the Contextual Fear Conditioning (CFC), the importance of hippocampus is well shown in retrograde amnesia models (trauma/lesion after learning event), but anterograde amnesia models (trauma/lesion occured before learning) are not so affected by hippocampus manipulation. There is a hypothesis proposing that other regions, also involved in spatial information processing, are somehow compensating the lack of hippocampus or just acquiring enough information to promote the aversive learning in CFC. These regions have intimate interaction with hippocampus, but cover a large cortical area, and some subcortical. Because of this, investigating how this compensation of hippocampal absence, as what is learned in this situation becomes complicated by traditional methods that manipulates one region at a time. Thus, methods that allow to assess the dynamics of connectivity in a more global manner, as networks analysis by Graph Theory, show great value to better understand the phenomena in question. Graph Theory, as a network analysis method for structural and functional data, has been increasingly adopted in neuroscience, providing a new and promising perspective, even in CFC preparations. The present study aims to analyze, using graph theory methods, the functional connectivity of animals with hippocampal lesion and use this analysis to infer differences and similarities (compared to control animais) of the network that may be underlying contextual fear learning. The data used to build the network will be based on the immuno-staining of the CREB protein (phosphorylated, pCREB), which is involved in neuronal plasticity induction after learning. We expect that the models built by the graph theory analysis will allow us to make new data-driven hypotheses which can be tested more specifically. (AU)