The neural signature of the decision value of future pain

Abstract

Pain is a primary driver of action. We often must voluntarily accept pain to gain rewards. Conversely, we may sometimes forego potential rewards to avoid associated pain. In this study, we investigated how the brain represents the decision value of future pain. Participants (n = 57) performed an economic decision task, choosing to accept or reject offers combining various amounts of pain and money presented visually. Functional MRI (fMRI) was used to measure brain activity throughout the decision-making process. Using multivariate pattern analyses, we identified a distributed neural representation predicting the intensity of the potential future pain in each decision and participants’ decisions to accept or avoid pain. This neural representation of the decision value of future pain included negative weights located in areas related to the valuation of rewards and positive weights in regions associated with saliency, negative affect, executive control, and goal-directed action. We further compared this representation to future monetary rewards, physical pain, and aversive pictures and found that the representation of future pain overlaps with that of aversive pictures but is distinct from experienced pain. Altogether, the findings of this study provide insights on the valuation processes of future pain and have broad potential implications for our understanding of disorders characterized by difficulties in balancing potential threats and rewards.

Keywords: MVPA; decision-making; pain; reward; value.

Fig. 1.

Experimental task and behavioral results. (A) Schematic representation of an exemplar experimental trial. For each trial, participants first saw an offer screen indicating the amount of money or pain involved in the next decision, followed by an interstimulus interval (ISI) and a decision screen indicating a complementary amount of pain (or money). Participants could accept or reject the offer by pressing a key. If accepted, participants received both the pain and the opportunity to gain the money at the end of the experiment. If rejected, participants received no pain and lost the chance to earn the money. (B) The average proportion of offers accepted and (C) average response time as a function of pain and money levels offered. Matrices in B and C were smoothed with a Gaussian kernel for display only.

Fig. 2.

Validation and performance of the multivariate pattern predicting pain offer levels and its spatial distribution. (A) Relationship between the actual and predicted pain offer level for each participant (shown in different shades of blue) and the corresponding distribution of the Pearson correlation between actual and predicted levels for each participant. (B) Average z-scored pattern similarity between the multivariate pattern and the parametric maps corresponding to each pain offer level. (C) Binary classification accuracy for different combinations of pain offer levels using the pain pattern similarity. The black dashed horizontal line shows the chance accuracy level. (D) Average z-scored pattern similarity between the multivariate pattern and the parametric maps corresponding to the level of pain risk in an independent dataset. (E) Multivariate pattern predicting the pain offer level thresholded at FDR q < 0.05 using a bootstrap distribution built from 10,000 samples drawn with replacement. The color bar shows the regression weights z-scored using the bootstrap distribution. Note that the multivariate patterns were thresholded for display and interpretation only. All error bars show the SEM. The full unthresholded weight map is available at https://neurovault.org/collections/10410. (F) Correlation between the pattern weights and resting-state (Left) cortical and (Right) striatal networks.

Fig. 3.

Validation and performance of the multivariate pattern predicting (A–D) money offer levels (MVP) and (E–H) shock intensity (SIP) and their spatial distribution. A and E show the relationship between the actual and predicted money offer level or shock intensity for each participant (shown in different shades of green or orange) and the corresponding distribution of the Pearson correlation between actual and predicted levels for each participant. B and F show the average pattern similarity between the multivariate pattern and the parametric maps corresponding to each money offer level or shock intensity. C and G show binary classification accuracy for different pairs of money offer levels using the MVP similarity and the combinations of pairs of shock intensity using the SIP similarity. The black dashed horizontal line shows the chance accuracy level. D and H show multivariate patterns predicting the money offer level and the shock intensity thresholded at FDR q < 0.05 using a bootstrap distribution built from 10,000 samples drawn with replacement. The color bars show the regression weights z-scored using the bootstrap distribution. Note that the multivariate patterns were thresholded for display and interpretation only. The full unthresholded weight maps are available at https://neurovault.org/collections/10410.

Fig. 4.

Cross-prediction of pain and monetary offers. Pattern similarity and discrimination accuracy between different levels for the (A) MVP applied to each level of the pain offers and (B) the PVP applied to each level of the money offers. (C) Results for the whole-brain cross-prediction searchlight analysis in which a principal component regression was trained on pain or money offers and tested on the other modality (P < 0.05, FWE corrected using a bootstrap distribution built from 5,000 samples drawn with replacement). Unthresholded striatum z-scored weights for the (D) PVP and the (E) MVP.

Fig. 5.

Cross-prediction of pain offers and physical pain. Pattern similarity and discrimination accuracy between different levels for the (A) PVP applied to each level of the shock intensities and (B) the SIP applied to each level of the pain offers. (C) The whole-brain cross-prediction searchlight analysis in which a principal component regression was trained on pain offers or shock intensities and tested on the other modality (P < 0.05, FWE corrected using a bootstrap distribution built from 5,000 samples drawn with replacement).

Fig. 6.

Cross-prediction of pain offers and intensity of aversive pictures. Pattern similarity and discrimination accuracy between different levels for the (A) PVP applied to each level of affective ratings and (B) the PINES applied to each level of the pain offers. (C) The whole-brain cross-prediction searchlight analysis in which a principal component regression was trained on pain offers or shock intensities and tested on the other modality (P < 0.05, FWE corrected using a bootstrap distribution built from 5,000 samples drawn with replacement).

Fig. 7.

Prediction of participants’ choices using pain and money offer patterns. (A–C) Pattern expression during the decision phase of the pain (A) and money patterns (B) developed in the first part of the offers as well as their difference (C). Matrices were smoothed with a Gaussian kernel for display only. (D) Decision surface of the SVM classifier trained on the pattern expression of the pain and money patterns during the decision. (E) Classification accuracies of SVM classifiers predicting participants’ decisions based on the expression of the pain and money patterns or the combination of both. Boxes show the quartiles of the dataset, while the whiskers extend to show the range of the distribution. The diamonds show the mean accuracy across all trials, and the raincloud plots show the distribution of accuracies across participants. Error bars show the 95% confidence interval.