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The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the persons experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

Background and purpose: Advanced 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. 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, 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. 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 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. 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 probably be extended to other pain syndromes. [ABSTRACT FROM AUTHOR]

Abstract: Endogenous pain modulation, as tested by the conditioned pain modulation (CPM) protocol, is typically less efficient in patients with chronic pain compared with healthy controls. We aimed to assess whether CPM is less efficient in patients with painful diabetic polyneuropathy (DPN) compared with those with nonpainful DPN. Characterization of the differences in central pain processing between these 2 groups might provide a central nervous system explanation to the presence or absence of pain in diabetic neuropathy in addition to the peripheral one. Two hundred seventy-one patients with DPN underwent CPM testing and clinical assessment, including quantitative sensory testing. Two modalities of the test stimuli (heat and pressure) conditioned to cold noxious water were assessed and compared between patients with painful and nonpainful DPN. No significant difference was found between the groups for pressure pain CPM; however, patients with painful DPN demonstrated unexpectedly more efficient CPMHEAT (-7.4 ± 1.0 vs -2.3 ± 1.6; P = 0.008). Efficient CPMHEAT was associated with higher clinical pain experienced in the 24 hours before testing (r = -0.15; P = 0.029) and greater loss of mechanical sensation (r = -0.135; P = 0.042). Moreover, patients who had mechanical hypoesthesia demonstrated more efficient CPMHEAT (P = 0.005). More efficient CPM among patients with painful DPN might result from not only central changes in pain modulation but also from altered sensory messages coming from tested affected body sites. This calls for the use of intact sites for proper assessment of pain modulation in patients with neuropathy. [ABSTRACT FROM AUTHOR]