ArticleEuropean journal of neurology2023
Electroencephalography functional connectivity-A biomarker for painful polyneuropathy.
Article in European journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Chronic pain and oscillatory neuroplasticity: Review on impaired compensatory network responses in resting-state electroencephalography.Brain network disorders · 2026Article
- Innovative strategies for diabetic peripheral neuropathy: From clinical management to emerging bioengineering solutions.Bioactive materials · 2026Review
- Cellular and molecular basis of neuropathic pain.RMD open · 2026Review
- Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- Exploring electroencephalographic chronic pain biomarkers: a mega-analysis.EBioMedicine · 2025Article
- Pain when it "counts": hurdle analysis of clinical pain ratings improves data model performance.Pain reports · 2025Article
- Advances and challenges in neuroimaging-based pain biomarkers.Cell reports. Medicine · 2024Review
- Big data, big consortia, and pain: UK Biobank, PAINSTORM, and DOLORisk.Pain reports · 2023Article
- In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?Frontiers in neuroscience · 2023Review
- Electroencephalography functional connectivity-A biomarker for painful polyneuropathy.European journal of neurology · 2023Article
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18 authors.
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Abstract
background and purposeAdvanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data-driven, predictive and explanatory approach is presented to discriminate painful from non-painful diabetic polyneuropathy (DPN) patients.
methodsThree 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, the 10 functional bivariate connections best differentiating between painful and non-painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters.
resultsPredictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non-painful polyneuropathy patients, with area under the receiver operating characteristic curve 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 for 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.
conclusionsResting 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 probably be extended to other pain syndromes.
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