Pain classification using functional near infrared spectroscopy and assessment of virtual reality effects in cancer pain management

Objective measurements of pain and safe methods to alleviate it could revolutionize medicine. This study used functional near-infrared spectroscopy (fNIRS) and virtual reality (VR) to improve pain assessment and explore non-pharmacological pain relief in cancer patients. Using resting-state fNIRS (rs-fNIRS) data and multinomial logistic regression (MLR), we identified brain-based pain biomarkers and classified pain severity in cancer patients. Participants included healthy individuals who underwent rs-fNIRS recording without VR (Group A), cancer patients who underwent rs-fNIRS recording both before and after engaging in the Oceania relaxation program VR intervention (Group B), and cancer patients who underwent rs-fNIRS recording without VR (Group C). All participants wore a wireless fNIRS headcap for brain activity recording. Pain severity was self-reported by patients using the FACES Pain Scale-Revised (FPS-R). fNIRS data were analyzed with MLR, categorizing pain into no/mild (0-4/10), moderate (5-7/10), and severe (8-10/10) levels. The MLR model classified pain severity in an unseen test group, selected using the leave-one-participant-out technique and repeated across all participants, achieving an accuracy of 74%. VR significantly reduced pain intensity (Wilcoxon signed-rank test, P < 0.001), with significant changes in brain functional connectivity patterns (P < 0.05). Additionally, 75.61% of patients experienced pain reductions exceeding the clinically relevant threshold of 30%. These findings underscore the potential of fNIRS for pain assessment and VR as a useful non-pharmacological intervention for cancer-related pain management, with broader implications for clinical pain management.