title: 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.
authors.affiliation: Computational Biology Center, T.J. Watson IBM Research Laboratory, Yorktown Heights, New York, USA. gcecchi@us.ibm.com
authors.forename: Guillermo A
authors.initials: GA
authors.lastname: Cecchi
authors.forename: Lejian
authors.initials: L
authors.lastname: Huang
authors.forename: Javeria Ali
authors.initials: JA
authors.lastname: Hashmi
authors.forename: Marwan
authors.initials: M
authors.lastname: Baliki
authors.forename: María V
authors.initials: MV
authors.lastname: Centeno
authors.forename: Irina
authors.initials: I
authors.lastname: Rish
authors.forename: A Vania
authors.initials: AV
authors.lastname: Apkarian
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D005260:Female; D006358:Hot Temperature / Q000009:adverse effects; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D010146:Pain / Q000523:psychology*; D058748:Pain Perception / Q000502:physiology*; D011601:Psychophysics / Q000379:methods*; D011897:Random Allocation; D012044:Regression Analysis