An fMRI-Based Neurologic Signature of Physical Pain

Abstract

BACKGROUND Persistent pain is measured by means of self-report, the sole reliance on which hampers diagnosis and treatment. Functional magnetic resonance imaging (fMRI) holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified. METHODS In four studies involving a total of 114 participants, we developed an fMRI-based measure that predicts pain intensity at the level of the individual person. In study 1, we used machine-learning analyses to identify a pattern of fMRI activity across brain regions — a neurologic signature — that was associated with heat-induced pain. The pattern included the thalamus, the posterior and anterior insulae, the secondary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray matter, and other regions. In study 2, we tested the sensitivity and specificity of the signature to pain versus warmth in a new sample. In study 3, we assessed specificity relative to social pain, which activates many of the same brain regions as physical pain. In study 4, we assessed the responsiveness of the measure to the analgesic agent remifentanil. RESULTS In study 1, the neurologic signature showed sensitivity and specificity of 94% or more (95% confidence interval [CI], 89 to 98) in discriminating painful heat from nonpainful warmth, pain anticipation, and pain recall. In study 2, the signature discriminated between painful heat and nonpainful warmth with 93% sensitivity and specificity (95% CI, 84 to 100). In study 3, it discriminated between physical pain and social pain with 85% sensitivity (95% CI, 76 to 94) and 73% specificity (95% CI, 61 to 84) and with 95% sensitivity and specificity in a forced-choice test of which of two conditions was more painful. In study 4, the strength of the signature response was substantially reduced when remifentanil was administered. CONCLUSIONS It is possible to use fMRI to assess pain elicited by noxious heat in healthy persons. Future studies are needed to assess whether the signature predicts clinical pain. (Funded by the National Institute on Drug Abuse and others.)

A Brain Phenotype for Stressor‐Evoked Blood Pressure Reactivity

“…In a final test of discriminative performance, a multivariate brain pattern developed for the prediction of self‐reported pain, the neurological pain signature,39 did not significantly predict ΔSBP across individuals ( r =0.08; P >0.38). This indicates that the multivariate brain pattern derived from our training sample and evaluated in our test sample is comparatively unique in its prediction of individual differences in stressor‐evoked SBP reactivity.…”

“…In a set of additional tests, we examined whether the whole‐brain, multivariate pattern, $\hat{\mathrm{\beta }}$, would predict individual differences in self‐reported affective or arousal changes, as well as behavioral task performance. Finally, we tested whether $\hat{\mathrm{\beta }}$ would outperform another widely used multivariate brain pattern developed for the prediction of an aversive behavioral state plausibly related to the experience of stress; namely, pain 39. If our multivariate brain pattern, but not 1 for pain, predicts SBP reactivity, then this would provide a line of discriminative evidence that our multivariate brain pattern uniquely accounts for individual differences in a dimension of cardiovascular stress reactivity.…”

Recent Progress in Understanding the Mechanisms of Pain and Itch: the Second Special Issue

“…In the last 5 years, the field has seen tremendous progress in the molecular and functional characterization of primary sensory neurons [6,7], neurocircuits of pain and itch [8][9][10], immune and glial modulation of pain and itch [11][12][13][14][15], molecular mechanisms of pain [16,17], and identification of brain signatures of pain [18]. Thus, it is timely to highlight the recent progress in a second special issue.…”

Neuroscience in court: The painful truth

“…As the researchers turn the plate's temperature up and down, they record the activity across different parts of the brain, including the sensory regions associated with the hand. From these patterns, Wager says, they can predict with better than 90% accuracy whether the plate is just warm or painfully hot 1 .…”