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

Abstract

While chronic pain assessment in household pets remains challenging, the use of non-invasive electroencephalography (EEG) in cats has shown promise to identify pain more objectively in this species. A novel EEG-based algorithm - Pain identification and quantification (Piq) - was originally developed in humans to quantify pain intensity. In this proof-of-concept study, the objective was to evaluate whether the Piq algorithm could be explored for feasibility to identify and quantify chronic osteoarthritic (OA) pain in cats. Adult neutered cats (n&#x202f;=&#x202f;5 including n&#x202f;=&#x202f;2 with osteoarthritis, OA) were assessed for their functional impairment (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 &#x2265;&#x202f;10&#x202f;% used as an exploratory threshold transferred from human studies. Pain-free cats showed gamma frequency band Piq scores <&#x202f;10&#x202f;% while OA cats exceeded 10&#x202f;% in both conscious and sedated conditions at Cz. Piq scores were negatively correlated with PWT, suggesting an increased neuro-sensitization with higher Piq scores. These preliminary findings suggest that the 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.