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

Area of Science:

• Neuroscience
• Neurophysiology
• Medical Imaging

Background:

• Chronic pain mechanisms are not fully understood.
• Persistent pain impacts millions globally.
• Neural signatures of chronic pain require identification.

Purpose of the Study:

• Investigate brain oscillations and connectivity in chronic pain.
• Identify neural signatures for chronic pain diagnosis.
• Explore the role of the default mode network (DMN) in chronic pain.

Main Methods:

• Analyzed electroencephalography (EEG) data from 42 chronic pain patients and 42 controls.
• Utilized network-based statistics for connectivity analysis.
• Employed machine learning to assess neural signatures as biomarkers.

Main Results:

• Chronic pain patients showed decreased high-frequency power but enhanced gamma synchronization.
• Increased gamma synchronization within the DMN was observed, particularly in a state with stronger intra-cingulate connections.
• A machine learning model achieved 75.5% accuracy in differentiating patients from controls using DMN connectivity features.

Conclusions:

• Enhanced DMN gamma synchronization is crucial in chronic pain pathophysiology.
• DMN gamma synchronization may serve as a valuable neural marker for chronic pain.
• This study offers new insights into the neural underpinnings of chronic pain.