Wager et al used machine-learning analyses to identify an anatomic pattern of functional magnetic resonance imaging (fMRI) activity across brain regions, termed a neurologic signature, which was associated with heat-induced pain. In four studies involving a total of 114 participants, thermal stimuli were applied in randomized sequences of varying intensity (trials) to the left forearm of each participant during fMRI scanning. Participants in study 1 underwent 12 trials at each of four escalating heat intensities, which were calibrated for each person from innocuous warmth to three levels of painful heat. The degree of pain that was experienced was rated by each participant. Participants in study 2 underwent a total of 75 trials across six temperatures (44.3° to 49.3° C in 1° C increments), judging whether the temperature stimulus was painful after each trial. Participants in study 3 recently had been romantically rejected, and during escalating heat stimuli and scanning trials, viewed an image of their ex-partner (denoted as "rejecter" trials, which elicit social pain) or 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. Warmth and pain stimuli were applied during each of two infusion series, one in which analgesic use was acknowledged (open-infusion series) and another in which analgesic use was incorrectly denied (hidden-infusion series). In study 1, a machine-learning-based regression technique, LASSO-PCR (least absolute shrinkage and selection operator-regularized principal components regression), was used to predict pain reports from the fMRI activity. The fMRI neurologic signature included significant positive weights in regions including the bilateral dorsal posterior insula, the secondary somatosensory cortex, the anterior insula, the ventrolateral and medial thalamus, the hypothalamus, and the dorsal anterior cingulate cortex. The signature response increased nonlinearly with increasing stimulus intensity during thermal stimulation; but as expected, it was uniformly low for the pain-anticipation and pain-recall periods. The neurologic signature showed sensitivity and specificity of 94% or more (95% confidence interval [CI], 89-98) in discriminating painful heat from non-painful warmth, pain anticipation, and pain recall. In study 2, the signature discriminated between painful heat and non-painful warmth with 93% sensitivity and specificity (95% CI, 84-100). The signature response increased monotonically across the six temperatures, with an expected nonlinear increase with temperature, and it correlated with both the reported level of pain and the stimulus temperature. In study 3, the fMRI signature discriminated between physical pain and social pain with 85% sensitivity (95% CI, 76-94) and 73% specificity (95% CI, 61-84). The neurologic signature response was substantially stronger for physical pain than for any of the other conditions (warmth, rejecter, or friend). Discrimination between the rejecter and friend conditions was no better than would be expected by chance. In study 4, the strength of the signature response was substantially reduced when remifentanil was administered, in parallel with increases in the drug effect-site concentration. [ABSTRACT FROM AUTHOR]