All-mode splice-loss estimation and splice-quality evaluation in few-mode fibers from a fundamental-mode power drop.

PURPOSE: The purpose of this paper was to explore the relationship between preoperative EEG frequency band characteristics and postoperative acute pain in patients undergoing spinal surgery, in order to provide a scientific basis for personalized pain management.

METHODS

The study included 78 patients undergoing spinal surgery under general anesthesia. Two hours before surgery, the resting EEG of the patients was collected for 10 minutes with eyes closed and relaxed, and the power spectrum characteristics of different frequency bands were extracted by time-frequency analysis. Collected pain scores, anxiety and sleep scales of all patients before and after surgery. Combined with statistical analysis methods for data processing, the correlation between preoperative EEG frequency band power and postoperative pain was explored.

RESULTS

Our study revealed that preoperative beta and gamma power in patients undergoing spinal surgery were negatively correlated with postoperative pain and demonstrated significant predictive value for pain outcomes (β: ρ = -0.84, P<0.001; γ: ρ = -0.85, P<0.001). Notably, γ-band due to their specific association with the temporal-insular network, exhibited the most robust predictive efficacy. Furthermore, preoperative β-band and γ-band were also negatively correlated with postoperative anxiety and sleep quality.

CONCLUSION

Preoperative β and γ oscillations represent promising neurophysiological correlates of acute pain susceptibility following spine surgery. Our findings suggest that these EEG metrics could serve as one of the components in a future multi-factorial predictive model for personalized pain management. However, given the multifactorial nature of pain and the lack of validated clinical thresholds, further research is required to establish their practical utility in guiding perioperative analgesic strategies. Future studies should focus on integrating these EEG biomarkers with psychological and clinical assessments to develop robust prediction tools.