Electroencephalography functional connectivity-A biomarker for painful polyneuropathy.

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.

A Biomarker for Discriminating Between Migraine With and Without Aura: Machine Learning on Functional Connectivity on Resting-State EEGs.

Advanced analyses of electroencephalography (EEG) are rapidly becoming an important tool in understanding the brain's processing of pain. To date, it appears that none have been explored as a way of distinguishing between migraine patients with aura (MWA) vs. those without aura (MWoA). In this work, we apply a mixture of predictive, e.g., classification methods and attribute-selection techniques, and traditional explanatory, e.g., statistical, analyses on functional connectivity measures extracted from EEG signal acquired from at-rest participants (N = 52) during their interictal period and tested them against the distinction between MWA and MWoA. We show that a functional connectivity metric of EEG data obtained during resting state can serve as a sole biomarker to differentiate between MWA and MWoA. Using the proposed analysis, we not only have been able to present high classification results (average classification of 84.62%) but also to discuss the underlying neurophysiological mechanisms upon which our technique is based. Additionally, a more traditional statistical analysis on the selected features reveals that MWoA patients show higher than average connectivity in the Theta band (p = 0.03) at rest than MWAs. We propose that our data-driven analysis pipeline can be used for resting-EEG analysis in any clinical context.

Fusing enacted and expected mimicry generates a winning strategy that promotes the evolution of cooperation.

Although cooperation and trust are essential features for the development of prosperous populations, they also put cooperating individuals at risk for exploitation and abuse. Empirical and theoretical evidence suggests that the solution to the problem resides in the practice of mimicry and imitation, the expectation of opponent's mimicry and the reliance on similarity indices. Here we fuse the principles of enacted and expected mimicry and condition their application on two similarity indices to produce a model of mimicry and relative similarity. Testing the model in computer simulations of behavioral niches, populated with agents that enact various strategies and learning algorithms, shows how mimicry and relative similarity outperforms all the opponent strategies it was tested against, pushes noncooperative opponents toward extinction, and promotes the development of cooperative populations. The proposed model sheds light on the evolution of cooperation and provides a blueprint for intentional induction of cooperation within and among populations. It is suggested that reducing conflict intensities among human populations necessitates (i) instigation of social initiatives that increase the perception of similarity among opponents and (ii) efficient lowering of the similarity threshold of the interaction, the minimal level of similarity that makes cooperation advisable.