EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task

Abstract

In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of “chronic primary pain” (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.