Large-scale neural network computations and multivariate representations during approach-avoidance conflict decision-making

Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities. Computational modeling supported a modified reinforcement learning model where participants integrated reward and threat value into a combined total value according to an individually varying policy parameter, which was highly predictive of decisions to approach reward vs avoid threat during trials where the highest reward option was also the highest threat option (i.e., approach-avoidance conflict). ICA analyses demonstrated unique roles for salience, frontoparietal, medial prefrontal, and inferior frontal networks in differential encoding of reward vs threat prediction error and value signals. The left frontoparietal network uniquely encoded degree of conflict between reward and threat value at the time of choice. MVPA demonstrated that delivery of reward and threat could accurately be decoded within salience and inferior frontal networks, respectively, and that decisions to approach reward vs avoid threat were predicted by the relative degree to which these reward vs threat representations were active at the time of choice. This latter result suggests that navigating AAC decisions involves generating mental representations for possible decision outcomes, and relative activation of these representations may bias subsequent decision-making towards approaching reward or avoiding threat accordingly.

Asymmetric representation of aversive prediction errors in Pavlovian threat conditioning

Survival in biological environments requires learning associations between predictive sensory cues and threatening outcomes. Such aversive learning may be implemented through reinforcement learning algorithms that are driven by the signed difference between expected and encountered outcomes, termed prediction errors (PEs). While PE-based learning is well established for reward learning, the role of putative PE signals in aversive learning is less clear. Here, we used functional magnetic resonance imaging in humans (21 healthy men and women) to investigate the neural representation of PEs during maintenance of learned aversive associations. Four visual cues, each with a different probability (0, 33, 66, 100%) of being followed by an aversive outcome (electric shock), were repeatedly presented to participants. We found that neural activity at omission (US-) but not occurrence of the aversive outcome (US+) encoded PEs in the medial prefrontal cortex. More expected omission of aversive outcome was associated with lower neural activity. No neural signals fulfilled axiomatic criteria, which specify necessary and sufficient components of PE signals, for signed PE representation in a whole-brain search or in a-priori regions of interest. Our results might suggest that, different from reward learning, aversive learning does not involve signed PE signals that are represented within the same brain region for all conditions.

Neural mediators of subjective and autonomic responding during threat learning and regulation

Threat learning elicits robust changes across multiple affective domains, including changes in autonomic indices and subjective reports of fear and anxiety. It has been argued that the underlying causes of such changes may be dissociable at a neural level, but there is currently limited evidence to support this notion. To address this, we examined the neural mediators of trial-by-trial skin conductance responses (SCR), and subjective reports of anxious arousal and valence in participants (n = 27; 17 females) performing a threat reversal task during ultra-high field functional magnetic resonance imaging. This allowed us to identify brain mediators during initial threat learning and subsequent threat reversal. Significant neural mediators of anxious arousal during threat learning included the dorsal anterior cingulate, anterior insula cortex (AIC), and ventromedial prefrontal cortex (vmPFC), subcortical regions including the amygdala, ventral striatum, caudate and putamen, and brain-stem regions including the pons and midbrain. By comparison, autonomic changes (SCR) were mediated by a subset of regions embedded within this broader circuitry that included the caudate, putamen and thalamus, and two distinct clusters within the vmPFC. The neural mediators of subjective negative valence showed prominent effects in posterior cortical regions and, with the exception of the AIC, did not overlap with threat learning task effects. During threat reversal, positive mediators of both subjective anxious arousal and valence mapped to the default mode network; this included the vmPFC, posterior cingulate, temporoparietal junction, and angular gyrus. Decreased SCR during threat reversal was positively mediated by regions including the mid cingulate, AIC, two sub-regions of vmPFC, the thalamus, and the hippocampus. Our findings add novel evidence to support distinct underlying neural processes facilitating autonomic and subjective responding during threat learning and threat reversal. The results suggest that the brain systems engaged in threat learning mostly capture the subjective (anxious arousal) nature of the learning process, and that appropriate responding during threat reversal is facilitated by participants engaging self- and valence-based processes. Autonomic changes (SCR) appear to involve distinct facilitatory and regulatory contributions of vmPFC sub-regions.

The interaction between stress and chronic pain through the lens of threat learning

Stress and pain are interleaved at multiple levels - interacting and influencing each other. Both are modulated by psychosocial factors including fears, beliefs, and goals, and are served by overlapping neural substrates. One major contributing factor in the development and maintenance of chronic pain is threat learning, with pain as an emotionally-salient threat - or stressor. Here, we argue that threat learning is a central mechanism and contributor, mediating the relationship between stress and chronic pain. We review the state of the art on (mal)adaptive learning in chronic pain, and on effects of stress and particularly cortisol on learning. We then provide a theoretical integration of how stress may affect chronic pain through its effect on threat learning. Prolonged stress, as may be experienced by patients with chronic pain, and its resulting changes in key brain networks modulating stress responses and threat learning, may further exacerbate these impairing effects on threat learning. We provide testable hypotheses and suggestions for how this integration may guide future research and clinical approaches in chronic pain.