The Role of Plasma Extracellular Vesicles in Remote Ischemic Conditioning and Exercise-Induced Ischemic Tolerance

Ischemic conditioning and exercise have been suggested for protecting against brain ischemia-reperfusion injury. However, the endogenous protective mechanisms stimulated by these interventions remain unclear. Here, in a comprehensive translational study, we investigated the protective role of extracellular vesicles (EVs) released after remote ischemic conditioning (RIC), blood flow restricted resistance exercise (BFRRE), or high-load resistance exercise (HLRE). Blood samples were collected from human participants before and at serial time points after intervention. RIC and BFRRE plasma EVs released early after stimulation improved viability of endothelial cells subjected to oxygen-glucose deprivation. Furthermore, post-RIC EVs accumulated in the ischemic area of a stroke mouse model, and a mean decrease in infarct volume was observed for post-RIC EVs, although not reaching statistical significance. Thus, circulating EVs induced by RIC and BFRRE can mediate protection, but the in vivo and translational effects of conditioned EVs require further experimental verification

Model Order Estimation for Independent Component Analysis of Epoched EEG Signals

Abstract: In analysis of multi-channel event related EEG signals indepedent component analysis (ICA) has become a widely used tool to attempt to separate the data into neural activity, physiological and non-physiological artifacts. High density elctrode systems offer an opportunity to estimate a corresponding large number of independent components (ICs). However, too large a number of ICs leads to overfitting of the ICA model, which can have a major impact on the model validity. Consequently, finding the optimal number of components in the ICA model is an important problem. In this paper we present a method for model order selection, based on a probabilistic framework. The proposed method is a modification of the Molgedey Schuster (MS) algorithm to epoched, i.e. event related data. Thus, the contribution of the present paper can be summarized as follows: 1) We advocate MS as a low complexity ICA alternative for EEG. 2) We define an epoch based likelihood function for estimation of a principled unbiased ’test error’. 3) Based on the unbiased test error measure we perform model order selection for ICA of EEG. Applied to a 64 channel EEG data set we were able to determine an optimum order of the ICA model and to extract 22 ICs related to the neurophysiological stimulus responses as well as ICs related to physiological- and non-physiological noise. Furthermore, highly relevant high frequency response information was captured by the ICA model.