An Efficient Implementation of the Synchronization Likelihood Algorithm for Functional Connectivity

Epileptogenic modulation index and synchronization in hypsarrhythmia of West syndrome secondary to perinatal arterial ischemic stroke

OBJECTIVE

Perinatal arterial ischemic stroke (PAIS) is associated with epileptic spasms of West syndrome (WS) and long term Focal epilepsy (FE). The mechanism of epileptogenic network generation causing hypsarrhythmia of WS is unknown. We hypothesized that Modulation index (MI) [strength of phase-amplitude coupling] and Synchronization likelihood (SL) [degree of connectivity] could interrogate the epileptogenic network in hypsarrhythmia of WS secondary to PAIS.

METHODS

We analyzed interictal scalp electroencephalography (EEG) in 10 WS and 11 FE patients with unilateral PAIS. MI between gamma (30-70 Hz) and slow waves (3-4 Hz) was calculated to measure phase-amplitude coupling. SL between electrode pairs was analyzed in 9-frequency bands (5-delta, theta, alpha, beta, gamma) to examine inter- and intra-hemispheric connectivity.

RESULTS

MI was higher in affected hemispheres in WS (p = 0.006); no differences observed in FE. Inter-hemispheric SL of 3-delta, theta, alpha, beta, gamma bands was significantly higher in WS (p < 0.001). In WS, modified Z-Score of intra-hemispheric SL values in 3-delta, theta, alpha, beta and gamma in the affected hemispheres were significantly higher than those in the unaffected hemispheres (p < 0.001) as well as 0.5-4 Hz (p = 0.004).

CONCLUSIONS

The significantly higher modulation in affected hemisphere and stronger inter- and intra-hemispheric connectivity generate hypsarrhythmia of WS secondary to PAIS.

SIGNIFICANCE

Epileptogenic cortical-subcortical transcallosal networks from affected hemisphere post-PAIS provokes infantile spasms.

High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network.

The sleep network organization during slow-wave sleep is more stable with age and has small-world characteristics more marked than during REM sleep in healthy men

Sleep plays an important role in cognitive functioning. However, few studies have investigated the sleep network organization. The aim of our study was to empirically investigate the presence and the stability with age of a small-world network organization during REM and slow-wave sleep using the effective connectivity measured by the Granger causality. Polysomnographic data from 30 healthy men recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by the Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realised by importing these connectivity matrices into the EEGNET Toolbox. Even if all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep, slow-wave sleep seems to have a small-world network organization more marked than REM sleep. Moreover, the sleep network organization is affected greater by age during REM sleep than during slow-wave sleep. In healthy individuals, the highlighting of a sleep network organization during slow-wave sleep more stable with age and with small-world characteristics more marked than during REM sleep may help to better understand the global and local processing of information during sleep.

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity

Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function.