2016
DOI: 10.7554/elife.15166
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Somatic and vicarious pain are represented by dissociable multivariate brain patterns

Abstract: Understanding how humans represent others’ pain is critical for understanding pro-social behavior. ‘Shared experience’ theories propose common brain representations for somatic and vicarious pain, but other evidence suggests that specialized circuits are required to experience others’ suffering. Combining functional neuroimaging with multivariate pattern analyses, we identified dissociable patterns that predicted somatic (high versus low: 100%) and vicarious (high versus low: 100%) pain intensity in out-of-sam… Show more

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Cited by 222 publications
(367 citation statements)
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References 108 publications
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“…Candidate brain regions include AI and ACC, given their involvement in processing both one’s own and observed pain (e.g. Corradi-Dell’Acqua et al, 2011; Decety and Jackson, 2004; Krishnan et al, 2016; Lamm et al, 2011; Singer et al, 2004), in pain prediction and anticipation (Atlas et al, 2010), and in observational learning of fear (Olsson et al, 2007). …”
Section: Social Information Effects On Pain and Emotionmentioning
confidence: 99%
“…Candidate brain regions include AI and ACC, given their involvement in processing both one’s own and observed pain (e.g. Corradi-Dell’Acqua et al, 2011; Decety and Jackson, 2004; Krishnan et al, 2016; Lamm et al, 2011; Singer et al, 2004), in pain prediction and anticipation (Atlas et al, 2010), and in observational learning of fear (Olsson et al, 2007). …”
Section: Social Information Effects On Pain and Emotionmentioning
confidence: 99%
“…Vicarious and somatic types of pain have commonalities at the neural level [15; 49] with overlapping activations in the dorsal anterior cingulate cortex, anterior insula, and other areas. Nevertheless, these activations appear to be unspecific to pain and rather related to negative affect [16; 31]. …”
Section: Potential Mechanisms Of Observationally-induced Pain Changesmentioning
confidence: 99%
“…Machine learning might lock onto features that correlate with pain, such as salience, rather than pain itself -the reverse inference problem 72,73 (discussed further below). Second, successful prediction of pain does not imply that the predictive brain biomarker is specific to the experience of pain; such a neuromarker must be tested in many types of painful and non-painful conditions to empirically establish what it does and does not respond to 54,[74][75][76] . Third, an imaging neuromarker might not generalize to all types of pain, or to all individuals; this aspect must also be tested empirically.…”
Section: Imaging Of Painmentioning
confidence: 99%