An fMRI-Based Neurologic Signature of Physical Pain

By Tor D. Wager, Lauren Y. Atlas, Martin A. Lindquist, M. Roy, Choong-Wan Woo, Ethan Kross. New England Journal of Medicine, 2013

1,418 citations recorded by the source corpus.

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.)

Subfields: Cognitive Neuroscience, Pharmacology, Physiology

View Paper: https://www.semanticscholar.org/paper/8d1c588d202f150e1797ed113fba7e67bfa43ecb

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Citations in the corpus

Shape shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits. (2013, background): “, 2011) [note, however, the recent strong evidence for a brain activity signature for acute pain (Wager et al., 2013)], with neurobiological theories (McIntosh, 2000; Sporns et al.”
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models (2018, neutral): “In particular, the General Linear Model (GLM) [Friston et al., 1994], searchlight analysis [Kriegeskorte et al., 2006] and whole-brain Least Absolute Shrinkage Logistic Regression (whole-brain Lasso) [Wager et al., 2013, Grosenick et al., 2013].”
Insects have the capacity for subjective experience (2016, background): “This cortical response to anaesthesia is similar to the analgesia-mediated reductions in activity of the cortical regions associated with pain (Wager et al., 2013).”
Cross‐validation failure: Small sample sizes lead to large error bars (2017, neutral): “In a remarkable example of predictive models using pooled data, Wager et al. (2013) were able to combine multiple pain studies to extract a neural signature specific to physical pain, discriminating it from social pain or warmth.”
Why fish do not feel pain (2016, background): “The cortex is anatomically defined by a number of distinct lobes (e.g., frontal, occipital, temporal, insular and parietal), and each of these lobes is further subdivided on the basis of anatomical cytoarchitecture and functional properties.”
From brain maps to cognitive ontologies: informatics and the search for mental structure (2015, background): “…on decoding of specific stimulus or task features within a single individual, but subsequent work has shown that one can decode large-scale psychological functions from fMRI data in a way that generalizes across individuals (Mourão-Miranda et al. 2005, Shinkareva et al. 2008, Wager et al. 2013).”
Emotion and the Prefrontal Cortex: An Integrative Review (2017, background): “…et al., 1999; Lieberman & Eisenberger, 2015; Livneh & Paz, 2012; Milad et al., 2007; Mobbs et al., 2009; Mobbs et al., 2010; Phelps, Delgado, Nearing, & LeDoux, 2004; Rainville et al., 1997; Rushworth et al., 2007; Shackman et al., 2011; Vogt, 2005; Wager et al., 2013; Wager et al., 2004).”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, neutral): “…least squares (OLS) regression (Rosenberg et al., 2015; Shen et al., 2017), least absolute shrinkage and selection operator (LASSO) regression (Wager et al., 2013), ridge regression (Siegel et al., 2016), elastic-net regression (Cui et al., 2018), linear support vector regression (LSVR)…”
The relation between statistical power and inference in fMRI (2017, background): “to predict pain [76] and Power and inference in neuroimaging”