Scholtes, I. | Search bioRxiv

bioRxiv2024

Enhanced behavioural and neural sensitivity to punishments in chronic pain and fatigue

Chronic pain and fatigue in musculoskeletal disease contribute significantly to disability, and recent studies suggest an association with reduced motivation and excessive fear avoidance. In this behavioural neuroimaging study in chronic inflammatory arthritis participants and healthy controls, we aimed to identify the specific behavioral and neural changes associated pain and fatigue during reward and loss decision-making. Computational modeling of behaviour identified a parametric signature, characterized most notably by increased punishment sensitivity. This signature is distinct from patterns previously reported in psychiatric conditions and it aligns with predictions of mechanistic models of chronic pain such as the fear avoidance model. Neural activity associated with the punishment prediction error was enhanced in the right posterior insular cortex, putamen, pallidum, and dorsolateral prefrontal cortex. Functional network connectivity analysis showed that insula centrality correlated with subjective reports of fatigue and pain. Overall, the findings show that pain and fatigue in chronic pain relate to objective behavioural changes, and can be mapped to a specific pattern of activity in brain circuits of motivation and decision-making.

neuroscience↗

bioRxiv2019

Sub-Optimal Learning of Tactile-Spatial Predictions in Patients with Complex Regional Pain Syndrome

In Complex Regional Pain Syndrome (CRPS), tactile sensory deficits have motivated the therapeutic use of sensory discrimination training. However, the hierarchical organisation of the brain is such that low-level sensory processing can be dynamically influenced by higher-level knowledge, e.g. knowledge learnt from statistical regularities in the environment. It is unknown whether the learning of such statistical regularities is impaired in CRPS. Here, we employed a hierarchical Bayesian model of predictive coding to investigate statistical learning of tactile-spatial predictions in CRPS. Using a sensory change-detection task, we manipulated bottom-up (spatial displacement of a tactile stimulus) and top-down (probabilistic structure of occurrence) factors to estimate hierarchies of prediction and prediction error signals, as well as their respective precisions or reliability. Behavioural responses to spatial changes were influenced by both the magnitude of spatial displacement (bottom-up) and learnt probabilities of change (top-down). The Bayesian model revealed that patients predictions (of spatial displacements) were found to be less precise, deviating further from the ideal (statistical optimality) compared to healthy controls. This imprecision was less context-dependent, i.e. more enduring across changes in probabilistic context and less finely-tuned to statistics of the environment. This caused greater precision on prediction errors, resulting in predictions that were driven more by momentary spatial changes and less by the history of spatial changes. These results suggest inefficiencies in higher-order statistical learning in CRPS. This may have implications for therapies based on sensory re-training whose effects may be more short-lived if success depends on higher-order learning.

neuroscience↗