Neural network underlying fear conditioning acquisition with temporal and spatial discontinuity: interactions of prelimbic cortex

Abstract

Stimuli to be associated frequently overlap, but it is also possible to associate stimuli discontinuously in time, as shown in trace conditioning for a discrete stimulus. This learning probably requires that the brain continues representing the stimulus after its presentation and involves the pre-limbic cortex (PL). Because the environment is also a powerful CS and inevitably associated, it may also be subject to trace fear conditioning. Recent work on our research group tested this hypothesis. We showed that a contextual representation can be maintained and associated to an aversive stimulus after 5s of temporal and spatial discontinuity. Additionally, this depends on PL activity, differently from contextual fear conditioning (CFC), in which stimuli overlap. To better understanding how a contextual representation remains in a temporal interval and is associated to a posterior aversive stimulus, the present study aims to investigate the neural network underlying acquisition of CFC and CFC with temporal discontinuity by functional and structural analysis. It will be investigated the requirement of interactions of PL to hippocampus (related to forming a contextual representation) and of PL to amygdala (related to associate stimuli), as well as to other structures involved with trace or CFC. We will further explore this question by measuring the activity and coordinated activity of 43 selected brain regions. A neural network underlying CFC and CFC with temporal discontinuity will be constructed and analyzed its proprieties and patterns of connectivity. Part of the functional organization of this network will be investigated. It will be verified the activity of neuronal populations with projections to PL or amygdala after both conditioning protocols. Together, these experiments intend to allow a better understanding of the influence of a spatial and temporal discontinuity in the neural network underlying CMC acquisition. (AU)