Differential Roles of the Salience Network During Prediction Error Encoding and Facial Emotion Processing Among Female Adolescent Assault Victims

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging(2018)

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摘要
BACKGROUND: Early-life assaultive violence exposure is a potent risk factor for posttraumatic stress disorder (PTSD) and other mood and anxiety disorders. Neurocircuitry models posit that increased risk is mediated by heightened emotion processing in a salience network including the dorsal anterior cingulate cortex, anterior insula, and amygdala. However, the processes of reinforcement learning (RL) also engage the salience network and are implicated in responses to early-life trauma and PTSD. To define their relative roles in response to early-life trauma and PTSD symptoms, the current study compared engagement of the salience network during emotion processing and RL as a function of early-life assault exposure. METHODS: Adolescent girls (n = 30 girls who had previously been physically or sexually assaulted; n = 30 healthy girls for comparison) 11 to 17 years of age completed two types of tasks during functional magnetic resonance imaging: a facial emotion processing task and an RL task using either social or nonsocial stimuli. Independent component analysis was used to identify a salience network and characterize its engagement in response to emotion processing and prediction error encoding during the RL tasks. RESULTS: Assault was related to greater reactivity of the salience network during emotion processing. By contrast, we found lesser encoding of negative prediction errors in the salience network, particularly during the social RL task, in girls who had been assaulted. The dysfunction of salience network activity during emotion processing and prediction error encoding was not associated with PTSD symptoms. CONCLUSIONS: These results suggest that hyper- versus hypoactivity of the salience network among trauma-exposed youths depends on the cognitive-affective domain.
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关键词
Childhood Trauma,Salience Network,Functional Neuroimaging,Reinforcement Learning,Emotion
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