Personalized biometrics of physical pain agree with psychophysics by participants with sensory over responsivity

Jihye Ryu, Tami Bar-Shalita, Yelena Granovsky, Irit Weissman-Fogel, Elizabeth B. Torres

Research output: Contribution to journalArticlepeer-review

Abstract

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

Original languageEnglish
Article number93
Pages (from-to)1-17
Number of pages17
JournalJournal of Personalized Medicine
Volume11
Issue number2
DOIs
StatePublished - Jan 2021

Bibliographical note

Funding Information:
Funding: This research was funded by The Nancy Lurie Marks Family Foundation Career Development Award to E.B.T. and by the Fellowship of Excellence in Computational and Data Science by Rutgers Discovery Informatics Institute to J.R.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • EEG
  • Micro-movements spikes
  • Pain biometrics
  • Personalized pain
  • Sensory over responsivity
  • Standardized scale
  • Stochastic analyses

ASJC Scopus subject areas

  • Medicine (miscellaneous)

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