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.

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).

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.