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Predictive dynamics of human pain perception.


ABSTRACT: While the static magnitude of thermal pain perception has been shown to follow a power-law function of the temperature, its dynamical features have been largely overlooked. Due to the slow temporal experience of pain, multiple studies now show that the time evolution of its magnitude can be captured with continuous online ratings. Here we use such ratings to model quantitatively the temporal dynamics of thermal pain perception. We show that a differential equation captures the details of the temporal evolution in pain ratings in individual subjects for different stimulus pattern complexities, and also demonstrates strong predictive power to infer pain ratings, including readouts based only on brain functional images.

SUBMITTER: Cecchi GA 

PROVIDER: S-EPMC3486880 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Predictive dynamics of human pain perception.

Cecchi Guillermo A GA   Huang Lejian L   Hashmi Javeria Ali JA   Baliki Marwan M   Centeno María V MV   Rish Irina I   Apkarian A Vania AV  

PLoS computational biology 20121025 10


While the static magnitude of thermal pain perception has been shown to follow a power-law function of the temperature, its dynamical features have been largely overlooked. Due to the slow temporal experience of pain, multiple studies now show that the time evolution of its magnitude can be captured with continuous online ratings. Here we use such ratings to model quantitatively the temporal dynamics of thermal pain perception. We show that a differential equation captures the details of the tem  ...[more]

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