Brainstem networks construct threat probability and prediction error from neuronal building blocks

Cognitive maps and the magnetic sense in vertebrates

Navigation requires a network of neurons processing inputs from internally generated cues and external landmarks. Most studies on the neuronal basis of navigation in vertebrates have focused on rats and mice and the canonical senses vision, hearing, olfaction, and somatosensation. Some animals have evolved the ability to sense the Earth's magnetic field and use it for orientation. It can be expected that in these animals magnetic cues are integrated with other sensory cues in the cognitive map. We provide an overview of the behavioral evidence and brain regions involved in magnetic sensing in support of this idea, hoping that this will guide future experiments.

Integration of Prior Expectations and Suppression of Prediction Errors During Expectancy-Induced Pain Modulation: The Influence of Anxiety and Pleasantness

Pain perception arises from the integration of prior expectations with sensory information. Although recent work has demonstrated that treatment expectancy effects (e.g., placebo hypoalgesia) can be explained by a Bayesian integration framework incorporating the precision level of expectations and sensory inputs, the key factor modulating this integration in stimulus expectancy-induced pain modulation remains unclear. In a stimulus expectancy paradigm combining emotion regulation in healthy male and female adults, we found that participants' voluntary reduction in anticipatory anxiety and pleasantness monotonically reduced the magnitude of pain modulation by negative and positive expectations, respectively, indicating a role of emotion. For both types of expectations, Bayesian model comparisons confirmed that an integration model using the respective emotion of expectations and sensory inputs explained stimulus expectancy effects on pain better than using their respective precision. For negative expectations, the role of anxiety is further supported by our fMRI findings that (1) functional coupling within anxiety-processing brain regions (amygdala and anterior cingulate) reflected the integration of expectations with sensory inputs and (2) anxiety appeared to impair the updating of expectations via suppressed prediction error signals in the anterior cingulate, thus perpetuating negative expectancy effects. Regarding positive expectations, their integration with sensory inputs relied on the functional coupling within brain structures processing positive emotion and inhibiting threat responding (medial orbitofrontal cortex and hippocampus). In summary, different from treatment expectancy, pain modulation by stimulus expectancy emanates from emotion-modulated integration of beliefs with sensory evidence and inadequate belief updating.

Noncortical cognition: integration of information for close-proximity behavioral problem-solving

Animals face behavioral problems that can be conceptualized in terms of a gradient of spatial and temporal proximity. I propose that solving close-proximity behavioral problems involves integrating disparate types of information in complex and flexible ways. In this framework, the midbrain periaqueductal gray (PAG) is understood as a key region involved in close-proximity motivated cognition. Anatomically, the PAG has access to signals across the neuroaxis via extensive connectivity with cortex, subcortex, and brainstem. However, the flow of signals is not unidirectional, as the PAG projects to the cortex directly, and further ascending signal flow is attained via the midline thalamus. Overall, the anatomical organization of the PAG allows is to be a critical hub engaged in cognition "here and now".

The Rescorla-Wagner model, prediction error, and fear learning

The Rescorla-Wagner model remains one of the most important and influential theoretical accounts of the conditions under which Pavlovian learning occurs. Moreover, the experimental approaches that inspired the model continue to provide powerful behavioral tools to advance mechanistic understanding of how we and other animals learn to fear and learn to reduce fear. Here we consider key features of the Rescorla-Wagner model as applied to study of fear learning. We review evidence for key insights of the model. First, learning to fear and learning to reduce fear are governed by a common, signed prediction error. Second, this error drives variations in effectiveness of the shock US that are causal to whether and how much fear is learned or lost during a conditioning trial. We also consider behavioral and neural findings inconsistent with the model and which will be essential to understand and advance understanding of fear learning.

Optogenetic inhibition of the caudal substantia nigra inflates behavioral responding to uncertain threat and safety

Defensive responding is adaptive when it approximates current threat, but maladaptive when it exceeds current threat. Here we asked if the substantia nigra, a region consistently implicated in reward, is necessary to show appropriate levels of defensive responding in Pavlovian fear discrimination. Rats received bilateral transduction of the caudal substantia nigra with halorhodopsin or a control fluorophore, and bilateral ferrule implants. Rats then behaviorally discriminated cues predicting unique foot shock probabilities (danger, p =1; uncertainty, p =0.25; and safety, p =0). Green-light illumination (532 nm) during cue presentation inflated defensive responding of halorhodopsin rats - measured by suppression of reward seeking - to uncertainty and safety beyond control levels. Green-light illumination outside of cue presentation had no impact on halorhodopsin or control rat responding. The results reveal caudal substantia nigra cue activity is necessary to inhibit defensive responding to non-threatening and uncertain threat cues.

Positioning the brainstem within the neural network of threat prediction

In a recent study, Strickland and McDannald dissected the role of brainstem networks in threat prediction. Using probabilistic threat discrimination in rats, the authors demonstrated that brainstem neurons estimate threat probability and generate positive aversive prediction errors after unexpected outcomes. Their findings suggest that, beyond organizing defensive behaviors, brainstem neurons are involved in threat prediction computations.