Magnetoencephalographic Spectral Abnormalities and Explainable Machine Learning of Neuropathic-Like Pain in Knee Osteoarthritis.

Objective: Neuropathic-like pain (NLP) in knee osteoarthritis (KOA) is associated with greater pain severity, poorer function, and less favorable outcomes, but its neurophysiological basis remains unclear. This study aimed to characterize magnetoencephalographic (MEG) spectral differences between NLP and nociceptive pain (NoCP) in KOA and evaluate whether interpretable machine-learning models could discriminate these pain phenotypes. Methods: In this cross-sectional study, 192 patients with KOA were classified as having NLP (n=55) or NoCP (n=137) using modified painDETECT questionnaire scores. All participants underwent pain-evoked MEG recording. Forty-five spectral features were extracted using the Welch method, including theta-, alpha-, beta-, and gamma-band relative power and peak alpha frequency (PAF). Between-group differences were assessed using Mann-Whitney Results: Compared with NoCP, NLP was associated with higher Visual Analogue Scale and Western Ontario and McMaster Universities Osteoarthritis Index scores, while other clinical characteristics were similar. NLP showed increased theta- and gamma-band relative power, decreased alpha-band relative power, and slower PAF, mainly in central, frontocentral, and frontal regions. These patterns remained largely unchanged after adjustment. Logistic regression performed best, with an area under the receiver operating characteristic curve of 0.768 (95% CI, 0.745-0.789) and balanced accuracy of 0.699 (95% CI, 0.675-0.722). Conclusion: Patients with NLP defined using the modified painDETECT questionnaire scores showed distinct MEG spectral alterations compared with those with NoCP. Interpretable MEG-based machine-learning models showed moderate ability to distinguish these questionnaire-defined pain phenotypes.