EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

RESEARCH ARTICLE EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome Mahmoud Hassan1,2*, Mohamad Shamas1,2,3, Mohamad Khalil3, Wassim El Falou3, Fabrice Wendling1,2 1 INSERM, U1099, Rennes, France, 2 Université de Rennes 1, LTSI, France, 3 Lebanese University, AZM Center for Biotechnology Research and Its Applications, Tripoli, Lebanon * Abstract OPEN ACCESS Citation: Hassan M, Shamas M, Khalil M, El Falou W, Wendling F (2015) EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome. PLoS ONE 10(9): e0138297. doi:10.1371/journal.pone.0138297 Editor: Bin He, University of Minnesota, UNITED STATES Received: May 18, 2015 Accepted: August 29, 2015 Published: September 17, 2015 Copyright: © 2015 Hassan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/. Data Availability Statement: All the data used here are available on the following public link https:// zenodo.org/record/10498 using the DOI:10.5281/ zenodo.10498. Introduction Funding: This work was supported by AZM and SAADE Association (Tripoli, Lebanon) and the Rennes University Hospital (COREC Project named conneXion, 2012–14). The work has also received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX07-01. Magneto/Electroencephalography (M/EEG) are key techniques to analyze functional connectivity from surface signals [1, 2] or/and from reconstructed brain sources [3, 4]. The main advantage of M/EEG is the excellent temporal resolution (sub-second) that offers the unique opportunity i) to track brain networks over very short duration which is the case in many cognitive tasks and ii) to analyze fast dynamical changes that can occur in brain disorders (like epileptic seizures for instance). So far, approaches based on graph theory have represented brain networks as sets of nodes interconnected by edges [5]. Once the nodes and edges are defined from the neuroimaging PLOS ONE | DOI:10.1371/journal.pone.0138297 September 17, 2015 1 / 20 EEGNET: The M/EEG Networks Tool Competing Interests: The authors have declared that no competing interests exist. data, algorithms based on graph theory can be applied to measure the topological properties of considered networks. The application of these algorithms on functional, as well as on structural connectivity matrices, have revealed many properties of brain networks, such as small-worldness [6, 7], modularity [8, 9], hubs [10] and rich-club configurations [11]. The graph theory based analysis has been widely used to characterize normal [12] and pathological [13] brain activities from several modalities. It has been used in many applications such as aging [14–16], Alzheimer’s disease [17–20], epilepsy [21–23], schizophrenia [24, 25] and autism [26]. In the M/EEG context, nodes represent either the electrodes or the dipole sources depending on whether the connectivity is analyzed at scalp or at reconstructed source level, respectively. The edges are defined by the values of the statistical dependencies among M/EEG signals or among reconstructed time courses of cortical sources. On the one hand, several tools were developed to process M/EEG signals such as EEGLAB [27], CARTOOL [28], Fieldtrip [29] and Brainstorm [30]. On the other hand, many other tools have been proposed to analyze and visualize complex networks such as Brain Connectivity Toolbox (BCT) [31], BrainNet Viewer [32], the GCCA toolbox [33], the connectome mapper [34], Gephi [35], the connectome Viewer [36], the eConnectome [37], the Connectome Visualization Utility (CVU) [38] and GraphVar [39]. All these packages are typically specialized for processing a particular step in the whole pipeline aimed to identifying and characterizing brain networks. However, a tool that comprises the complete pipeline from M/EEG processing to analysis/visualization of brain networks is still missing. This consideration led us to develop and present EEGNET, MATLAB-based software with Graphical User Interface (GUI). Our main objective was to develop a complete framework that can cover most of the processing from EEG recordings to graph analysis and visualization. This pipeline includes: 1) loading and filtering the M/EEG signals, 2) the solution to the inverse problem and the reconstruction of the cortical sources, 3) the computation of the functional connectivity, 4) the calculation of the network measures and 5) the visualization of 2D (scalp level) and 3D (cortex level) brain networks and associated measures. Methods and Results EEGNET is a useful processing pipeline to identify, visualize and characterize brain networks from M/EEG recordings. It can perform all steps including the estimation of brain sources, the computation of the functional connectivity and the mapping of brain networks at scalp level and/or at source level. The basic workflow is shown in Fig 1. Overview The main elements of EEGNET are: The data. This file represents either the scalp EEG data or the reconstructed sources. The default file format is the ‘.mat’. It should be a 3 dimensional matrix (Nc x Ns x Nt) where Nc, Ns and Nt ar (...truncated)