Abstract Background Early adverse experiences are assumed to affect fundamental processes of reward learning and decision-making. However, computational neuroimaging studies investigating these circuits are sparse and limited to studies that investigated adversities retrospectively in adolescent samples. Methods We used prospective data from a longitudinal birth cohort study (n=156, 87 females, mean age=32.2) to investigate neurocomputational components underlying reinforcement learning in an fMRI-based passive avoidance task. We applied a principal component analysis to capture common variation across seven prenatal and postnatal adversity measures. The resulting adversity factors (factor 1: postnatal psychosocial adversities and prenatal maternal smoking, factor 2: prenatal maternal stress and obstetric adversity, and factor 3: lower maternal stimulation) and single adversity measures were then linked to computational markers of reward learning (i.e. expected value, prediction errors) in the core reward network. Results Using the adversity factors, we found that adversities were linked to lower expected value representation in striatum, ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC). Expected value encoding in vmPFC further mediated the relationship between adversities and psychopathology. In terms of specific adversity effects, we found that obstetric adversity was associated with lower prediction error signaling in the vmPFC and ACC, whereas lower maternal stimulation was related to lower expected value encoding in the striatum, vmPFC, and ACC. Conclusions Our results suggested that adverse experiences have a long-term disruptive effect on reward learning in several important reward-related brain regions, which can be associated with non-optimal decision-making and thereby increase the vulnerability of developing psychopathology.

Early life adversities affect expected value signaling in the adult brain