Eur J Neurol. 2022 Sep 23. doi: 10.1111/ene.15575. Online ahead of print.
BACKGROUND: Advanced analysis of EEG data becomes an essential tool in brain research. Based solely on resting state EEG signals, we present a data-driven, predictive and explanatory approach to discriminating painful from non-painful diabetic polyneuropathy (DPN) patients.
METHODS: Three minutes long, 64 electrode resting-state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory, and machine learning analyses. First, we identified the 10 functional bivariate connections best differentiating between painful and non-painful patients in each EEG band and calculated the relevant ROC. Later, those connections were correlated with selected clinical parameters.
RESULTS: Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non-painful polyneuropathy patients, with ROC curve AUC values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non-painful ones (p=0.008 for theta and p=0.001 alpha bands). Moreover, intra- band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands.
CONCLUSIONS: Resting-state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can likely be extended to other pain syndromes.