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

The Piq algorithm may effectively identify and quantify chronic OA pain in cats using EEG.

This proof-of-concept study aimed to evaluate the feasibility of using the Pain Identification and Quantification (Piq) algorithm, originally developed for humans, to identify and quantify chronic osteoarthritic (OA) pain in cats through electroencephalography (EEG). Five adult neutered cats, including two with OA, were assessed for functional impairment using the Montreal Instrument for Cat Arthritis Testing for use by Veterinarians (MI-CAT(V)) and neuro-sensitization at both peripheral (Paw Withdrawal Threshold, PWT) and spinal (response to mechanical temporal summation, RMTS) levels. Resting-state EEG recordings were acquired from Cz, C3/C4 under conscious and sedated conditions. The first five minutes of EEG data were analyzed using the Piq algorithm, with Piq scores ≥10% used as an exploratory threshold transferred from human studies. Pain-free cats showed gamma frequency band Piq scores <10%, while OA cats exceeded 10% in both conscious and sedated conditions at Cz. Piq scores were negatively correlated with PWT, suggesting increased neuro-sensitization with higher Piq scores. These preliminary findings suggest that the Piq algorithm may capture gamma-band EEG patterns potentially associated with chronic OA pain in cats, consistent with prior evidence in humans. Despite the small sample size, this study demonstrates the feasibility of applying a human EEG-based pain quantification algorithm to OA cats, supporting its potential for future cross-species translation.

This summary was distilled by AI and may occasionally misinterpret data. Confirm critical details with the primary literature before clinical application.