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EEG-based quantification of chronic pain in cats: A proof-of-concept study using the Piq algorithm.

Aliénor Delsart1, Colince Segning2, Aude Castel3

  • 1Groupe de recherche en pharmacologie animale du Québec (GREPAQ), Université de Montréal, Québec, Canada.

Veterinary Journal (London, England : 1997)
|February 22, 2026
PubMed
Summary

A novel electroencephalography (EEG) algorithm, Pain Identification and Quantification (Piq), shows promise for objectively assessing chronic osteoarthritic (OA) pain in cats. This cross-species application could improve pain management in feline companions.

Keywords:
EEG gamma band (Cz electrode)Montreal instrument for cat arthritis testing for use by veterinarians (MI-CAT(V))Osteoarthritis painPain identification and quantification (Piq)Paw withdrawal threshold (PWT)Quantitative sensory testing (QST)Response to mechanical temporal summation (RMTS)

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Area of Science:

  • Veterinary Neurology
  • Pain Management
  • Biomedical Engineering

Background:

  • Assessing chronic pain in cats is challenging, necessitating objective diagnostic tools.
  • Non-invasive electroencephalography (EEG) offers a potential method for objective pain evaluation in felines.
  • A human-derived EEG algorithm, Pain Identification and Quantification (Piq), quantifies pain intensity.

Purpose of the Study:

  • To evaluate the feasibility of the Piq algorithm for identifying and quantifying chronic osteoarthritic (OA) pain in cats.
  • To explore the potential for cross-species translation of EEG-based pain assessment from humans to felines.

Main Methods:

  • Resting-state EEG data were acquired from adult cats (n=5) under conscious and sedated conditions.
  • The Piq algorithm analyzed the first five minutes of EEG data, focusing on gamma frequency band activity.
  • Cats were assessed for functional impairment (MI-CAT(V)) and neuro-sensitization (PWT, RMTS).

Main Results:

  • Pain-free cats exhibited Piq scores below 10%, while OA cats exceeded this threshold in both conscious and sedated states.
  • Piq scores showed a negative correlation with Paw Withdrawal Threshold (PWT), indicating increased neuro-sensitization with higher scores.
  • The algorithm successfully differentiated pain levels based on EEG patterns in cats.

Conclusions:

  • The Piq algorithm demonstrates feasibility for detecting chronic OA pain in cats using EEG.
  • Findings suggest the algorithm captures gamma-band EEG patterns associated with feline OA pain, consistent with human studies.
  • This proof-of-concept study supports the potential for cross-species translation of EEG-based pain quantification.