General pain Scientific Publications

Latest publication 04/01/2026

Coding of mechanical pain by myelinated and unmyelinated nociceptors in human ha

Data are presented as individual and mean (±SEM) responses. To account for differences in how participants used the VAS, unpleasantness ratings...

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    [title] => Coding of mechanical pain by myelinated and unmyelinated nociceptors in human ha
    [paragraph] => Coding of mechanical pain by myelinated and unmyelinated nociceptors in human hairy skin
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Authors
Otmane Bouchatta, Oumie Thorell, Andrew G. Marshall, Merat Rezaei, Ahmed Barakat, Sarah McIntyre, Gregory J. Gerling, David A. Mahns, Håkan Olausson, Saad S. Nagi

 Lab

Journal
PAIN Reports
Abstract
Data are presented as individual and mean (±SEM) responses. To account for differences in how participants used the VAS, unpleasantness ratings were binarized (VAS > 0 = unpleasant) and expressed as incidence—the proportion of trials at each force level in which any unpleasantness was reported. Statistical analyses were conducted using GraphPad Prism (version 9.1.2, GraphPad Software Inc, La Jolla, CA). The specific statistical tests used are reported alongside the relevant results. Principal component analysis (PCA) was used to visualize relationships between neuronal units and their electrophysiological response variables. The data were analyzed twice: first, using units as a grouping variable, with the 3 response metrics (number of spikes, mean frequency, and peak frequency) across indentation forces. Second, using both unit type and indentation forces as grouping variables, and the 3 metrics as response variables. This 2-level approach enabled visualization of variability associated with both unit type and stimulus intensity. Missing values were imputed using a custom function that replaced missing entries with the mean response of other units of the same type at the same indentation force. This imputation was performed separately for each neural metric, preserving unit type-specific patterns while minimizing data loss. To quantify the heterogeneity of unit type across indentation forces, we calculated the mean Euclidean distance to the centroid of each group in the PCA space. For each unit type and force level, the centroid was defined as the mean coordinate along the first 2 principal components (PC1, PC2). The mean distance to centroid (and its standard deviation) for each group provided a measure of within-group dispersion, representing the degree of heterogeneity in neuronal responses. Data wrangling, analysis, and visualization for the PCA were performed in R programming language (version 4.5.1) using the packages tidyverse (v 2.0.0) for data wrangling, FactoMineR (v 2.12) for PCA analysis, factoextra (v 1.07) for PCA visualization, missMDA (v 1.20) for handling the missing data, and ggplot2 (v 3.5.2) for data visualization.
Keywords/Topics
pain reports data; unpleasantness ratings; controlled mechanical stimulation; differences; participants; incidence; trials; each force level; which; any unpleasantness
BIOSEB Instruments Used:
Von Frey Filaments (BIO-VF-M)
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