Unified measures quantifying intensity and similarity of pain and somatosensory percepts
Journal article, 2025

Across somatosensory and pain literature, there exist several methods of characterizing the location and extent of perceived sensations, and quantifying how these sensory maps may differ. However, these measures of somatosensory intensity and similarity can give non-unique results, creating challenges in literature review and meta-analysis across different methods. In this paper, we propose novel and unifying measures to quantify the similarity and intensity of pain maps and somatosensory percepts. These measures are generalizable and can be applied to any application of somatosensory maps and are usable with both discretized and free-hand drawings in both two-dimensional and three-dimensional representations. Somatosensory percept intensity (SPI) is inspired by Piper’s law, which describes the phenomenon of incomplete spatial summation wherein changes in pain area do not yield linearly proportional changes in perceived intensity. Somatosensory percept deviation (SPD) is derived from optimal transport theory, which quantifies differences between two probability distributions or somatosensory maps. Mathematical derivations for both measures are provided. The utility of these measures is demonstrated using data from two studies, one characterizing elicited somatosensory percepts, and one investigating neuropathic pain drawings. The proposed measures strongly agree with the validation studies, illustrating their potential as agnostic measures for characterizing somatosensory percepts in studies and meta-analyses. Ultimately, our work yields powerful unified measures for use in the fields of perception and pain and may aid in improved pain characterization within healthcare, granting a better understanding of the needs and progression of patients experiencing pain.

somatosensory

pain intensity

pain measure

percept

pain map

Author

Eric Earley

University of Colorado

Colorado School of Public Health

Chalmers, Electrical Engineering, Systems and control

Malin Ramne

Chalmers, Electrical Engineering, Systems and control

Johan Wessberg

University of Gothenburg

Journal of Neurophysiology

0022-3077 (ISSN) 1522-1598 (eISSN)

Vol. 134 1 292-302

Subject Categories (SSIF 2025)

Neurosciences

Neurology

DOI

10.1152/jn.00031.2025

PubMed

40522894

More information

Latest update

7/29/2025