Resting-State Magnetoencephalography Functional Connectivity in Cervical Spondylotic Myelopathy: An MEG Study with SHAP-Based Interpretation

The diagnosis of cervical spondylotic myelopathy (CSM) relies mainly on clinical symptoms and structural imaging, highlighting the need for objective functional biomarkers. This study investigated alterations in resting-state magnetoencephalography (MEG) functional connectivity in CSM and evaluated whether multiband weighted phase lag index (wPLI) features could distinguish CSM patients from healthy controls (HCs). Eyes-closed resting-state MEG data were acquired from 31 CSM patients and 32 HCs. Region-of-interest-level wPLI connectivity was calculated in the theta, alpha, beta, and gamma bands and used to train multiple machine learning classifiers. Model performance was assessed using nested group cross-validation, and SHapley Additive exPlanations (SHAP) were used to interpret the best-performing model. Patients with CSM exhibited frequency-specific connectivity alterations, particularly in the theta and gamma bands. Logistic regression achieved the best overall discriminative performance, and SHAP analysis indicated that classification was driven mainly by long-range theta-band connections and gamma-band connections involving the frontal pole. These findings suggest that CSM is associated with measurable reorganization of large-scale cortical networks and that resting-state MEG connectivity combined with explainable machine learning may provide a promising framework for exploring candidate neurophysiological biomarkers of CSM.