Value Estimation and Latent-State Update-Related Neural Activity During Fear Conditioning Predict Posttraumatic Stress Disorder Symptom Severity

Learning theories of posttraumatic stress disorder (PTSD) purport that fear learning processes, such as those that support fear acquisition and extinction, are impaired. Computational models that aim to capture specific processes involved in fear learning have primarily assessed model free, or trial-and-error, reinforcement learning (RL). Although prior research indicates that aspects of model-free RL are disrupted among individuals with PTSD, models have yet to quantify and identify whether more nuanced, contextually driven learning is also disrupted. Given empirical evidence of aberrant contextual modulation of fear in PTSD, the present study sought to identify whether model-based RL processes are altered during fear conditioning among women with interpersonal violence (IPV)-related PTSD (n=85) using computational modeling. Several traditional models and a latent-state model that captured model-based RL were applied to skin conductance responses (SCR) collected during fear acquisition and extinction, and the latent-state model was identified as the best fitting model. Model-derived parameters from the latent-state model were carried forward to neuroimaging analyses (voxel-wise and independent component analysis) and results revealed that reduced latent-state related activity within visual processing regions uniquely predicted higher PTSD symptoms. Additionally, after controlling for latent-state update-related encoding, greater value estimation encoding within the left frontoparietal network during fear acquisition and reduced value estimation encoding within the dorsomedial prefrontal cortex during fear extinction predicted greater PTSD symptoms. Results provide evidence of disrupted model-based RL processes in women with IPV-related PTSD, which may contribute to difficulties revising fear and safety information. Future work should further assess model-based RL in PTSD