Corticostriatal circuits in the transition to chronic back pain: The predictive role of reward learning

Figures

• 

  Figure 1. Study design All participants underwent three phases of the experiment: habituation, acquisition, and extinction. During the habituation and extinction phases, both discriminative stimuli (DSpain and DSmoney, blue and yellow arrows on black background, respectively, duration: 6 s) were presented eight times each and followed by an inter-trial interval of 8–10 s (random jitter). During acquisition, twenty DS of each type were presented for 6 s and followed by an anticipation phase of 4 s. In the monetary reward condition, a monetary reward (1 €) or no reward (fixation cross) was subsequently presented for 3 s. In the pain-relief condition, the anticipation phase was always followed by a painful stimulus lasting 1.5 s. During the feedback phase of the pain-relief condition, participants received either pain relief (no pain) or pain (pain for 1.5 s). Participants were instructed to press a button with their index finger as soon as they saw any of the two discriminative stimuli, with the goal of winning a monetary reward or pain relief if they pressed the button “fast enough.” In reality, both rewards were delivered in 50% of the trials. Participants never received rewards if they…

• 

  Figure 2. Fronto-striatal encoding of prediction error signal predicts the transition from subacute to chronic pain (Left) Correlations of the percentage of change in pain severity from baseline to the 6 month follow up and the BOLD response to prediction errors in the NAc and vmPFC. The dashed vertical line indicates the 20% cutoff that was used to binarize the change in pain severity for the receiver operating characteristic (ROC) curves on the right side. BOLD responses were extracted as parameter estimates from predefined masks extracted from http://neurosynth.org (see STAR methods). Correlations are reported as Pearson’s correlation with Bonferroni-corrected p values (corrected for 45 tests, yielding a threshold of p < 0.001). (Right) We additionally divided patients in “recovered” if their pain severity decreased by 20% or more between the first examination and the follow-up assessment and “persistent” patients in all other instances. ROC curves were created for classifying recovered and persistent patients with the respective parameter estimates extracted from the regions of interest. We report the area under each ROC curve as an estimate of sensitivity and specificity. Associated p…

• 

  Figure 3. Functional connectivity of NAc and vmPFC independent of learning (i.e., resting-state functional connectivity) shows low predictive value of the transition from subacute to chronic pain (Left) Correlations of the percentage of change in pain severity from baseline to the 6 month follow up with the resting-state functional connectivity between vmPFC and bilateral NAc. The dashed vertical line indicates the 20% cutoff that was used to binarize the change in pain severity for the ROC curves on the right side. Resting-state functional connectivity was extracted as the correlation between individual time series in predefined masks, which were extracted from http://neurosynth.org (see STAR methods). Correlations are reported as Pearson’s correlation with Bonferroni-corrected p values (corrected for 2 tests, yielding a threshold of p < 0.025). (Right) Corresponding ROC curves were created for classifying recovered and persistent pain patients with the functional connectivity between vmPFC and NAc at rest. We report the area under each ROC curve as an estimate of sensitivity and specificity. Associated p values for the comparison to a chance-level ROC curve (i.e., AUC = 0.5) are reported…

• 

  Figure 4. Pattern of responses to the discriminative stimulus for pain relief in the right NAc predicts transition from subacute to chronic pain (A) Patterns of voxelwise t scores from the contrast DSpain (SABPp > SABPr). Warm colors depict voxels with higher responses in patients with persistent pain, while cool colors depict voxels with higher responses in recovered patients. (B) The corresponding ROC curve for classifying recovered and persistent-pain patients based on the multivariate classifiers. We report the area under the ROC curve as an estimate of sensitivity and specificity. Associated p values for the comparison to a chance-level ROC curve (i.e., AUC = 0.5) are reported as Bonferroni-corrected p values (corrected for 45 tests, yielding a threshold of p < 0.001). The boxplot additionally shows the probability of being classified as a patient with persistent pain for recovered patients (yellow) and patients with persistent pain at follow up (blue). The hinges of the boxplots represent the first and third quartiles, and the whiskers extend to the last value within 1.5 times the interquartile range. Single data points are shown as black circles. rNAc, right nucleus accumbens; DS,…

• 

  Figure 5. Patients with CBP are characterized by reduced vmPFC responses to prediction error and increased responses to a discriminative stimulus (A–C) (Left) BOLD responses to (A) positive monetary reward prediction error, (B) the discriminative stimulus for monetary rewards, and (C) functional connectivity between left NAc and vmPFC during positive monetary reward prediction error are depicted as p maps and were calculated using non-parametric two-sample t tests (CBP > HC shown in red and yellow, CBP < HC shown in blue and light blue) within a mask of vmPFC and NAc, with bootstrapping, using 5,000 permutations (all reward-learning contrasts that dissociated HC and CBP in vmPFC and NAc are shown). (Right) No significant correlations were found between pain severity in patients with CBP and the BOLD response in predefined masks of the vmPFC and NAc for the respective contrast. Outliers are depicted as empty circles and imputed values as triangles. See Tables S9 and S10 for all other associations between reward-learning contrasts and pain severity. vmPFC, ventromedial prefrontal cortex; lNAc, left nucleus accumbens; DS, discriminative stimulus; PPE, positive prediction error for monetary…

• 

  Figure 6. Functional connectivity of NAc and vmPFC at rest does not dissociate patients with chronic back pain and controls (A and B) ROC curves were created for classifying patients with chronic back pain and controls based on the functional connectivity between vmPFC and (A) right NAc and (B) left NAc, respectively. We report the area under each ROC curve as an estimate of sensitivity and specificity. Associated p values for the comparison to a chance-level ROC curve (i.e., AUC = 0.5) are reported as Bonferroni-corrected p values (corrected for 2 tests, yielding an uncorrected threshold of p < 0.025). Boxplots additionally show the distribution of patients with CBP (yellow) and controls (blue). The hinges of the boxplots represent the first and third quartiles, and the whiskers extend to the last value within 1.5 times the interquartile range. Single data points are shown as circles. vmPFC, ventromedial prefrontal cortex; lNAc, left nucleus accumbens; rNAc, right nucleus accumbens; BL, baseline; FU, follow up; AUC, area under the curve; RS, resting state; PE, parameter estimates.