Biology subjects

Hoskin, R.

Publications and source records attributed to Hoskin, R..

3 recordsLinked to original sources

bioRxiv2023

Interactions between the representations of pain and reward suggest dynamic shifts in reference point

With an aim to understand how brains compute the expected utility of mixed prospects, namely those associated with both negative and positive attributes, we designed a task which equated the opportunity to learn about these attributes and their hedonic value. Participants underwent fMRI scanning while they experienced a classical conditioning paradigm where emotionally-neutral faces predicted a probability of pain and reward conforming to a 2 (Electric Pain: high, low) x 2 (Monetary Reward: high, low) factorial design. We found a robust interaction between the anticipation of pain and reward in the BOLD signal. Analysis of simple effects revealed that sensitivity to each attribute increased under high levels of the other attribute. In the bilateral insula and mid-cingulate gyrus sensitivity to pain was greater under high reward, and in the OFC, caudate, ventral striatum and VTA sensitivity to reward was greater under high pain. We speculate that this pattern is due to dynamic shifts in the reference point participants considered to evaluate each attribute.

neuroscience↗

bioRxiv2021

Adaptive coding of pain prediction error in the anterior insula

BackgroundUnderstanding the mechanisms behind the influence of expectation and context on pain perception is crucial for improving analgesic treatments. Prediction error (PE) signals how much a noxious stimulus deviates from expectation and is therefore crucial for our understanding of pain perception. It is thought that the brain engages in adaptive coding of pain PE, such that sensitivity to unexpected outcomes is modulated by contextual information. While there is behavioural evidence that pain is coded adaptively, and evidence that reward PE signals are coded adaptively, controversy remains regarding the underlying neural mechanism of adaptively-coded pain PEs. MethodsA cued-pain task was performed by 19 healthy adults while undergoing FMRI scanning. BOLD responses to the task were tested using an axiomatic approach to identify areas that may code pain PE adaptively. ResultsThe left dorsal anterior insula demonstrated a pattern of response consistent with adaptively-coded pain PE. Signals from this area were sensitive to both predicted pain magnitudes on the instigation of expectation, and the unexpectedness of pain delivery. Crucially however, the response at pain delivery was consistent with the local context of the pain stimulation, rather than the absolute magnitude of delivered pain, a pattern suggestive of an adaptively-coded PE signal. ConclusionsThe study advances our understanding of the neural basis of pain prediction. Alongside existing evidence that the periaqueductal grey codes pain PE and the posterior insula codes pain magnitude, the results highlight a distinct contribution of the left dorsal anterior insula in the processing of pain. Significance statementAlthough there is behavioural evidence that pain is coded adaptively, the neural mechanisms serving this process are not well understood. This study used functional MRI to provide the first evidence that the left dorsal anterior insula, an area associated with aversive learning, responds to pain in a manner consistent with the adaptive coding of pain prediction error. This study aids our understanding of the neural basis of subjective pain representation, and thus can contribute to the advancement of analgesic treatments.

neuroscience↗

bioRxiv2017

Sensitivity to Pain Expectations: A Bayesian Model of Individual Differences

The thoughts and feelings people have about pain (referred to as pain expectations) are known to alter the perception of pain. However little is known about the cognitive processes that underpin pain expectations, or what drives the differing effect that pain expectations have between individuals. This paper details the testing of a model of pain perception which formalises the response to pain in terms of a Bayesian prior-to-posterior updating process. Using data acquired from a short and deception-free predictive cue task, it was found that this Bayesian model predicted ratings of pain better than other, simpler models. At the group level, the results confirmed two core predictions of predictive coding; that expectation alters perception and that increased uncertainty in the expectation reduces its impact on perception. The addition to the model of parameters relating to trait differences in pain expectation, improved its fit, suggesting that such traits play a significant role in perception beyond those expectations triggered by the pain cue. When model parameters were allowed to vary by participant, the models fit improved further. This final model produced a characterisation of each individuals sensitivity to pain expectations. This model is relevant for the understanding of the cognitive basis of pain expectations and could potentially act as a useful tool for guiding patient stratification and clinical experimentation.

neuroscience↗