Effect of connectivity measures on the identification of brain functional core network at rest.

Magneto/Electro-encephalography (M/EEG) source connectivity is an emergent tool to identify brain networks with high time/space resolution. Here, we aim to identify the brain core network (s-core decomposition) using dense-EEG. We also evaluate the effect of the functional connectivity methods used and more precisely the effect of the correction for the so-called source leakage problem. Two connectivity measures were evaluated: the phase locking value (PLV) and phase lag index (PLI) that supposed to deal with the leakage problem by removing the zero-lag connections. Both methods were evaluated on resting state dense-EEG signals recorded from 19 healthy participants. Core networks obtained by each method was compared to those computed using fMRI from 487 healthy participants at rest (from the Human Connectome Project - HCP). The correlation between networks obtained by EEG and fMRI was used as performance criterion. Results show that PLV networks are closer to fMRI networks with significantly higher correlation values with fMRI networks, than PLI networks. Results suggest caution when selecting the functional connectivity methods and mainly methods that remove the zero-lag connections as it can severely affect the network characteristics. The choice of functional connectivity measure is indeed crucial not only in cognitive neuroscience but also in clinical neuroscience.

Kernel based support vector machine for the early detection of syncope during head-up tilt test.

This study aims to analyze the autonomic nervous system response during head-up tilt test (HUTT), by exploring the changes in dynamic properties of heart rate variability in subjects with and without syncopes, to predict the outcome of HUTT. Baroreflex response, as well as linear and non-linear parameters of RR-interval time series, have been extracted from the ECG of 66 subjects: 35 with and 31 without syncope during HUTT. The results show that, when considering the first 15 min of tilting position, the total power spectrum, the standard deviation, the long-term fractal scale of RR-interval and ΔRR-interval of time series increase, while the sample entropy decreases in the positive group compared to the negative one. These indices may be good predictors of positive response in patients with reflex syncope. Additionally, an analysis of the first 15 min of tilting position using kernel support vector machines leads to a correct classification of 85% of patients, within negative and positive response groups (specificity = 80.6% and sensitivity = 88.5%). In medical applications, it is important to avoid false negative diagnosis of syncopes during HUTT. Taking this into account, an overall accuracy of 72.1% can be obtained in the same window allowing the reduction of the examination time in the clinical domain.