Human Studies

To investigate whether physician-patient agreement of potential patient problem areas impacts subsequent patient enrollment in an interdisciplinary pain management program.

Therapeutic interventions for neuropathic pain (NP), such as the NMDA-antagonist ketamine, can vary widely in effectiveness; Here, we conducted a longitudinal functional MRI study to test the hypothesis that the pain relieving effect of ketamine is due to reversal of abnormalities in regional low frequency brain oscillations (LFOs) and abnormal cross-network functional connectivity (FC) of the dynamic pain connectome. We found that: 1) ketamine decreased regional LFOs in the posterior cingulate cortex (PCC) of the default mode network (DMN), 2) a machine learning algorithm demonstrated that treatment-induced brain changes could be used to make generalizable inferences about pain relief, 3) treatment responders exhibited a significant decrease in cross-network static FC between the PCC and regions of the sensorimotor (SM) and salience networks following treatment, 4) the degree of reduced cross-network FC was correlated with the amount of pain relief, and 5) ketamine treatment did not produce significant differences in static or dynamic FC within the ascending nociceptive- or descending antinociceptive pathway. These findings support the proposition that regional LFOs contribute to cross-network connectivity that underlie the effectiveness of ketamine to produce significant relief of neuropathic pain. Together with our recent findings that pre-treatment dynamic FC of the descending antinociceptive pathway can predict ketamine treatment outcomes, these new findings indicate that pain relief from ketamine arises from a combination of a strong and flexible pre-treatment FC of the descending antinocieptive pathway together with plasticity (reduction) of cross-network connectivity of the DMN with SM and salience networks.

Most studies fail to show an association between higher levels of pain-related fear and protective movement behavior in patients with chronic low back pain (CLBP). This may be explained by the fact that only general measures of pain-related fear have been used to examine the association with movement patterns. This study explored whether task-specific, instead of general measures of pain-related fear can predict movement behavior.

Perception of sensory stimulation is influenced by numerous psychological variables. One example is placebo analgesia, where expecting low pain causes a painful stimulus to feel less painful. Yet, because pain evolved to signal threats to survival, it should be maladaptive for highly-erroneous expectations to yield unrealistic pain experiences. Therefore, we hypothesised that a cue followed by a highly discrepant stimulus intensity, which generates a large prediction error, will have a weaker influence on the perception of that stimulus. To test this hypothesis we collected two independent pain-cueing datasets. The second dataset and the analysis plan were preregistered ( https://osf.io/5r6z7/ ). Regression modelling revealed that reported pain intensities were best explained by a quartic polynomial model of the prediction error. The results indicated that the influence of cues on perceived pain decreased when stimulus intensity was very different from expectations, suggesting that prediction error size has an immediate functional role in pain perception.