High-Density Electroencephalographic Analysis of Neuropathic Pain - A Prospective, Multicenter Clinical Study

The absence of objective, biologically-based diagnostics for neuropathic pain (NP) is a fundamental barrier to precision medicine in neurology and psychiatry. Reliance on subjective self-report leads to misdiagnosis, heterogeneous patient cohorts in clinical trials, and an inability to objectively evaluate therapeutic efficacy. To address this, we leveraged the China Chronic Pain Cohort (CPCC) and high-density electroencephalography (HD-EEG) to identify objective neural signatures of NP. Our study enrolled 286 participants, including 117 NP patients, 56 non-neuropathic pain patients, and 113 healthy controls. HD-EEG analysis revealed distinct spatial functional connectivity patterns and neuronal oscillation coupling mechanisms characteristic of different pain categories. Multimodal analysis of HD-EEG revealed a distinct and reproducible neurophysiological signature of NP, characterized by aberrant oscillatory power and functional connectivity within a distributed network encompassing prefrontal, cingulate, and insular regions. Critically, we developed a novel Graph-Generative Network (GGN) model that translated these complex neural patterns into a clinically actionable tool. Our model differentiated NP from other pain types and healthy controls with exceptional accuracy (98%), and reliably predicted subjective pain intensity (VAS/NRS scores), effectively decoding a subjective state into an objective metric. These findings suggest that HD-EEG signatures can serve as objective biomarkers for NP, offering new avenues for objective pain assessment and personalized treatment strategies. Future research should focus on validating these biomarkers in larger cohorts and exploring their potential for broader clinical application.