Participants

The studies included a total of 114 healthy participants. Study 1 included 20 participants, 8 of whom were women; the mean (±SD) age was 28.8±7.5 years. Study 2 included 33 participants, 22 of whom were women; the mean age was 27.9±9.0 years. Study 3 included 40 participants, 21 of whom were women; the mean age was 20.8±2.6 years.6 Study 4 included 21 participants, 11 of whom were women; the mean age was 24.7±4.2 years.7 The Columbia University institutional review board approved all the studies, and all participants provided written informed consent. All the authors vouch for the accuracy and completeness of the data and analyses reported and the fidelity of the studies to the protocols. See the Supplementary Appendix, available with the full text of this article at NEJM.org, for additional details.

Study Design

In all four studies, we applied thermal stimuli in randomized sequences of varying intensity (trials) to the left forearm of each participant during fMRI scanning. For imaging, we used a 1.5-T General Electric scanner in studies 1, 3, and 4 and a 3-T Phillips scanner in study 2.

Participants in study 1 underwent 12 trials at each of four intensities, which were calibrated for each person: innocuous warmth (defined with the use of self-report by the participant as level 1 on a 9-point visual-analogue scale [VAS], with a mean [±SD] temperature of 41.0±1.9°C) and three levels of painful heat (participant-defined levels 3, 5, and 7, with mean temperatures of 43.3±2.1°C, 45.4±1.71°C, and 47.1±0.98°C, respectively). Each trial consisted of a warning cue and anticipation period (8 seconds), stimulation (10 seconds), and a pain-recall and rating period (4 seconds), with periods of rest before and after recall.

Participants in study 2 underwent a total of 75 trials across six temperatures (44.3 to 49.3°C in 1°C increments). After each trial, participants judged whether the stimulus was painful. They subsequently judged nonpainful warmth on a 100-point VAS and pain intensity on a 100-point VAS. Ratings were coded from 0 to 99 for nonpainful events and from 100 to 200 for painful events.

Participants in study 3 underwent 32 trials, consisting of 8 trials with each of four stimulus types. We delivered noxious heat (46.6±1.7°C, denoted “painful”) and warmth that was near the pain threshold (39.9±2.8°C, denoted “warm”) at temperatures calibrated for each person. Each participant had recently experienced a romantic breakup and continued to feel intensely rejected. During scanning, participants viewed an image of their ex-partner (denoted as “rejecter” trials, which elicit social pain8) and an image of a close friend (denoted as “friend” trials).

Participants in study 4 received two intravenous infusions of remifentanil, a potent μ-opioid agonist, during fMRI scanning in two series of trials. In the open-infusion series, participants knew they received remifentanil, and in the hidden-infusion series, they were told that no drug was delivered, even though it had been administered. Remifentanil doses (mean dose, 0.043±0.01 μg per kilogram of body weight per minute) were individually calibrated before the session to elicit analgesia without sedation, and we estimated the brain concentration of the drug over time using a pharmacokinetic model.9 We conducted 36 trials — 18 involving pain (mean temperature, 47.1±1.7°C) and 18 involving warmth (mean temperature, 41.2±2.6°C) — during each of the two infusion series. Drug infusion began partway through each series, after 6 trials, and ended after 24 trials. This design resulted in a continuously varying concentration of the drug over time during each infusion series.

Deriving the Signature

In study 1, we used a machine-learning–based regression technique, LASSO-PCR (least absolute shrinkage and selection operator-regularized principal components regression),10 to predict pain reports from the fMRI activity. We selected relevant brain areas a priori using the NeuroSynth meta-analytic database11 (see the Supplementary Appendix) and averaged the brain activity for each intensity level within each participant.12-14 We used the signal values from the voxels, each of which measured 3 mm3, in the a priori map to predict continuous pain ratings, using leave-one-participant-out cross-validation4 (see the Supplementary Appendix). The result was a spatial pattern of regression weights across brain regions, which was prospectively applied to fMRI activity maps obtained from new participants. Application of the signature to an activity map (e.g., a map obtained during thermal stimulation) yielded a scalar response value, which constituted the predicted pain for that condition.

Figure 1. Figure 1. Prediction of Physical Pain on the Basis of Normative Data from Other Participants in Study 1. Panel A shows the signature map, consisting of voxels in which activity reliably predicted pain. The map shows weights that exceed a threshold (a false discovery rate of q<0.05) for display only; all weights were used in prediction. ACC denotes anterior cingulate cortex, CB cerebellum, FUS fusiform, HY hypothalamus, IFJ inferior frontal junction, INS insula, MTG middle temporal gyrus, OG occipital gyrus, PAG periaqueductal gray matter, PCC posterior cingulate cortex, PFC prefrontal cortex, S2 secondary somatosensory cortex, SMA supplementary motor area, SMG supramarginal gyrus, SPL superior parietal lobule, TG temporal gyrus, and THAL thalamus. Direction is indicated with preceding lowercase letters as follows: a denotes anterior, d dorsal, i inferior, l lateral, m middle, mid mid-insula, p posterior, and v ventral. Panel B shows reported pain versus cross-validated predicted pain. Each colored line or symbol represents an individual participant. Panel C shows the signature response versus the pain intensity for heat, pain-anticipation, and pain-recall conditions. Signature-response values were calculated by taking the dot product of the signature-pattern weights and parameter estimates from a standard, single-participant general linear model, with regressors for each condition. The estimates shown are derived from cross-validation, so that signature weights and test data are independent. I bars indicate standard errors. The receiver-operating-characteristic plots in Panel D show the tradeoff between specificity and sensitivity. Lines are fitted curves, assuming gaussian signal distributions. The test of pain versus no pain and the forced-choice test are shown by dashed lines and solid lines, respectively. Performance on the forced-choice test was at 100% for all conditions; thus, the lines are overlapping.

We used permutation tests to obtain unbiased estimates of accuracy and bootstrap tests to determine which brain areas made reliable contributions to prediction (Figure 1). Stimulation did not elicit head movement, and head-movement estimates did not predict pain (for a description of head-movement analyses, see the Supplementary Appendix).

Predicting Pain in an Independent Sample

In study 2, we tested the neurologic signature identified in study 1, with no further model fitting, for the prediction of pain in individual participants, using data from a different scanner. We also estimated activity maps and signature responses for individual trials, which allowed us to use mixed-effects regression models to test the relationship between neurologic signature responses and intensity judgments during trials involving painful and nonpainful stimuli.

Testing for Specificity

In study 3, we applied the signature to activation maps that resulted from physical sensation (painful and warm conditions) and from viewing images related to social pain (rejecter and friend conditions).6

Response to Analgesic Treatment

In study 4, we tested the effects of stimulus intensity (painful vs. warm), administration of remifentanil (drug concentration), and manner of drug administration (open vs. hidden) on the signature response. For each of the open and hidden trial series, we estimated activation maps for painful stimulation, warm stimulation, and the magnitude of changes in each that followed the a priori time course of drug concentration from the pharmacokinetic model (Fig. S5 in the Supplementary Appendix). Because the drug concentration was continuous over time, the binary classification of painful versus warm conditions was based on the averages of the results of three trials before drug administration and three trials performed at the peak drug concentration.

Statistical Analysis

Table 1. Table 1. Pain-Classification Performance, According to Study.

We assessed the sensitivity and specificity of the signature for two kinds of decisions. In one test, the discrimination of pain from no pain, we compared the signature-response value (i.e., the strength of expression of the signature pattern) for one condition with a threshold, with a response over the threshold being classified as a pain response. Receiver-operating-characteristic plots traced the tradeoff of sensitivity and specificity at different thresholds (Figure 1D), and the threshold that minimized overall classification errors is reported (Table 1).

In forced-choice discrimination, two activation maps from the same participant were compared, and the image with the higher overall signature response (i.e., the stronger expression of the signature pattern) was classified as associated with more pain. Forced-choice tests are particularly suitable for fMRI because they do not compare the signature response with a threshold that is fixed across persons. Therefore, they do not require people to use the pain-reporting scale in the same way, and they do not require the scale of fMRI activity to be the same across scanners (see the Supplementary Appendix). Sensitivity, specificity, positive predictive value, and decision accuracy are all equivalent in the forced-choice test. The MATLAB code for implementing all analyses is available at http://wagerlab.colorado.edu/.