Abnormal Enhanced Gamma Synchronization in the Default Mode Network Associated With Chronic Pain

Chronic pain is characterized by persistent and often debilitating symptoms, yet its underlying neural mechanisms remain poorly understood. This study investigates alterations in brain oscillations and connectivity in chronic pain patients using electroencephalography (EEG), aiming to identify potential neural signatures of chronic pain.

This cross-sectional study analyzed EEG data from 42 chronic pain patients and 42 healthy controls to identify differences in oscillatory activity and network connectivity. Connectivity analyses were corrected for multiple comparisons using network-based statistics. Machine learning techniques were employed to evaluate the potential of these neural signatures as biomarkers for chronic pain.

Chronic pain patients exhibited decreased power in high-frequency bands. Conversely, functional connectivity analysis revealed widespread enhancements in gamma synchronization in chronic pain patients. Dynamic connectivity analysis demonstrated that chronic pain patients had significantly increased gamma synchronization within the default mode network (DMN), particularly in a dominant state characterized by stronger intra-cingulate connections. A machine learning model effectively differentiated patients from controls, achieving robust accuracy of 75.5% ± 6.9%, with sensitivity of 75.3% ± 12.6% and specificity of 80.0% ± 10.5%, primarily driven by the DMN connectivity features. Correlation analysis indicated that the connection between the left posterior cingulate and caudal anterior cingulate within the DMN was positively correlated with pain duration (p = 0.021, r = 0.354).

Enhanced gamma synchronization within the DMN plays a critical role in the pathophysiology of chronic pain. DMN gamma synchronization may serve as a valuable neural marker for chronic pain, providing new insights into its underlying mechanisms.