Implementation of dynamic threat belief updating within cerebro-spinal systems and opioidergic pathways

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Background: Learning to predict threats builds on integration of aversive, nociceptive, outcomes to form a threat belief. Yet, threat learning also captures dynamic updating of established threat beliefs, when the environment changes. To characterize both, formation and dynamic belief updating (DynBU), a neurocomputational model for statistical learning has been established. Yet, a statistical learning framework to delineate the neurobiological mechanisms of dynamic threat learning is missing. Aims: The overarching aim is to delineate the neural basis of dynamic updating of threat beliefs by deciphering the interplay between nociceptive outcome processing in the spinal cord, brain-stem mediated arousal, together with encoding of threat beliefs in forebrain circuits. This project further aims to delineate a neuropharmacological, opioidergic mechanism of dynamic threat belief updating.Hypotheses: We hypothesize (1) an interaction between encoding of nociceptive, aversive outcomes (i.e., spinal cord) and brainstem-mediated arousal (e.g., locus coeruleus, LC), which gates updating of threat beliefs via established brain circuits for threat processing (e.g., amygdala and dorsal anterior cingulate cortex, dACC). In particular, we hypothesize (2) that orchestration of defensive threat responses that are either proximal or distal requires a functional connection between brainstem mediated arousal (e.g., LC) and brain regions that encode proximity of threats (periaqueductal gray, PAG). We expect (3) that opioid-receptor blockade enhances aversive outcome integration, which can be simulated in learning models. Finally, we hypothesize (4) that this enhancement of aversive outcome integration by opioid-receptors blockade will be implemented in interactions between the PAG, amygdala and LC. Planned methods: Participants will a undergo a change-point task that requires faster and slower defense against proximal vs. distal predators (i.e., “predator task”, harmonized with project 8), while acquiring cerebro-spinal fMRI. Opioid-receptor effects on the predator task will first be simulated and tested in a second sample that undergoes brain fMRI. Expected impact: This project will provide psychological, neural, and computational mechanisms underlying DynBU of threats and thereby make a significant contribution to the major aims of this RU. Specifically, we will advance the proposed computational and neurobiological model of statistical learning by mechanism of threat learning. This project will further provide a unique contribution by identification of a neuropsychopharmacological mechanism that controls updating of threat beliefs in humans. Together with project 8, we will further be able to translate the results in healthy volunteers to the anxiety phenotype. Furthermore, distinct links with other projects allow to bridge between statistical learning of threats and the effects of stress (project 3) or statistical learning from rewarding outcomes (project 4).