Peak Alpha Frequency as a Neural Marker of Postoperative Pain Outcomes in Spinal Fusion Surgery

ObjectivePostoperative pain outcomes following spinal fusion surgery remain difficult to predict, as structural and surgical indicators alone offer limited insight into who will experience meaningful relief. A substantial proportion of patients continue to report persistent pain after surgery, underscoring the need for objective markers that can help identify those at risk of poor recovery. Peak alpha frequency (PAF) has emerged as a promising trait-like neural signature of pain sensitivity in experimental models, where individuals with slower PAF tend to exhibit heightened pain sensitivity. Yet despite this link, its ability to forecast longer-term postoperative pain trajectories remains unclear. MethodsSeventeen adults undergoing cervical or lumbar fusion surgery were included. Resting-state, eyes-closed EEG was recorded preoperatively and at multiple visits after surgery. PAF was extracted from central electrodes using the centre-of-mass method. Pain intensity was assessed longitudinally on standardised self-report pain scales. Associations between PAF measures and postoperative pain change were examined using correlation analyses, and receiver operating characteristic (ROC) analyses evaluated discrimination of pain responders ([≥]50% improvement). ResultsPreoperative peak alpha frequency (PAF) was positively associated with longer-term pain reduction at the 3-month follow-up, but showed no consistent relationship with early postoperative pain. Across pain measures, a consistent pattern emerged across the Brief Pain Inventory (BPI), visual analogue scale (VAS), and numerical rating scale (NRS), but not the verbal rating scale (VRS) or Short-Form McGill (SF-MPQ). At the 3-month follow-up, associations reached statistical significance for BPI-Worst ({rho} = 0.67, p = 0.017), and BPI-Average Pain ({rho} = 0.62, p = 0.033). VAS and NRS showed moderate-to-strong effects that approached significance in non-parametric analyses and were significant for VAS when treated as an approximately interval measure (Pearson r = 0.63, p = 0.022). ROC analyses using BPI-Worst pain improvement demonstrated good discriminative ability of preoperative PAF for identifying treatment responders at 3 months (AUC = 0.84; 95% CI: 0.61-1.00), with high specificity and moderate sensitivity at the Youden-optimal threshold of 10.11 Hz. By contrast, changes in PAF over time were not reliably related to changes in pain scores, suggesting that PAF functions more as a stable, trait-like predictor than a dynamic biomarker in this context. ConclusionThis study demonstrates the feasibility and potential clinical value of preoperative EEG for characterising individual differences in postoperative pain recovery following spinal fusion. The results identify faster preoperative PAF as a stable neural signal that captures meaningful variability in longer-term pain reduction, with convergent support across multiple patient-reported measures. While replication in a larger cohort is required, these findings establish a clear foundation for evaluating PAF as a candidate neurophysiological marker to inform preoperative risk profiling and potentially personalised perioperative pain-management strategies in spinal fusion patients.