Just back from SfN, here are 3 of the most interesting pain posters/talks from SfN (in no particualr order).
1. Schulz et al (Munich): Neurophysiological correlates of tonic pain. They record EEG with a tonic thermal pain stimulus that varies over time, and compare with phasic pain. They show the clearest evidence to date that tonic and phasic are fundamentally different - both from a neuranatomical and electrophysiological perspective. In particular, the prefrontal theta tracks subjective ratings of tonic pain which is dissociable from actual thermal input. It would be fascinating to see this in patients.
2. Baliki et al (Northwestern): Hub reorganization of brain functional networks: A hallmark of the chronic pain state. They take a graph theoretic approach to rsfMRI connectivity data, and show that chronic pain is charactersied by widespread hub disruption in nodes (small regions of interest) with a sparse link density. Basically, this means that there is a fundamental change in the basic functional architecture at a whole-brain level, manifest as a change in the small-worldliness, which seems to quantitatively and objectively characterise the functional chronic pain state for the first time. But what is even more remarkable is that exactly the same changes are seen in rat rsfMRI, developing slowly after injury, showing that this is a valid tranlsational pain biomarker. This will be a landmark paper whenever it is published.
3. Atlas et al (NYU): How instructed knowledge shapes aversive learning. In this nanosymposium talk, Lauren Atlas an co. dissociate distinct mechanisms underlying reversal learning driven either by feedback, or by instruction. Interestingly, SCRs appropriately follow the reversed contingencies following instruction, but the amygdala carries on signal feedback related teaching signals (associabilities) regardless, as if it is blind to this information. Furthermore, the ventral striatal prediction error signal is reduced, suggesting that somehow this error-based feedback circuit isn't used. What this data show is that understanding the relationship between experience based and cognitive based aversive learning is critical to developing advanced models of aversive (fear) learning.