Title |
Predictive Dynamics of Human Pain Perception
|
---|---|
Published in |
PLoS Computational Biology, October 2012
|
DOI | 10.1371/journal.pcbi.1002719 |
Pubmed ID | |
Authors |
Guillermo A. Cecchi, Lejian Huang, Javeria Ali Hashmi, Marwan Baliki, María V. Centeno, Irina Rish, A. Vania Apkarian |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Peru | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 67% |
Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 2% |
United States | 2 | 2% |
France | 1 | <1% |
Germany | 1 | <1% |
Unknown | 100 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 22 | 21% |
Researcher | 21 | 20% |
Student > Master | 14 | 13% |
Student > Bachelor | 7 | 7% |
Professor > Associate Professor | 7 | 7% |
Other | 20 | 19% |
Unknown | 15 | 14% |
Readers by discipline | Count | As % |
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Psychology | 19 | 18% |
Neuroscience | 17 | 16% |
Medicine and Dentistry | 14 | 13% |
Agricultural and Biological Sciences | 12 | 11% |
Computer Science | 9 | 8% |
Other | 19 | 18% |
Unknown | 16 | 15% |