Combining EEG source connectivity and network similarity: Application to object categorization in the human brain

A major challenge in cognitive neuroscience is to evaluate the ability of the human brain to categorize or group visual stimuli based on common features. This categorization process is very fast and occurs in few hundreds of millisecond time scale. However, an accurate tracking of the spatiotemporal dynamics of large-scale brain networks is still an unsolved issue. Here, we show the combination of recently developed method called dense-EEG source connectivity to identify functional brain networks with excellent temporal and spatial resolutions and an algorithm, called SimNet, to compute brain networks similarity. Two categories of visual stimuli were analysed in this study: immobile and mobile. Networks similarity was assessed within each category (intra-condition) and between categories (inter-condition). Results showed high similarity within each category and low similarity between the two categories. A significant difference between similarities computed in the intra and inter-conditions was observed at the period of 120-190ms supposed to be related to visual recognition and memory access. We speculate that these observations will be very helpful toward understanding the object categorization in the human brain from a network perspective.Comment: 5 pages, 2 figures. Accepted for 2016 IEEE Workshop on Statistical Signal Processin

Connectivité de sources en EEG-hr et dynamique des réseaux cérébraux fonctionnels

National audienceLe traitement l'information par le cerveau est un processus dynamique qui met en jeu une réorganisation rapide des réseaux cérébraux fonctionnels, sur une échelle de temps très courte (> nombre d'électrodes), (ii) estimer les dépendances statistiques (connectivité fonctionnelle) entre les sources reconstruites , (iii) caractériser les réseaux identifiés (sous forme des noeuds connectés par des liens formant un graphe) par des analyses basées sur la théorie des graphes et (vi) segmenter, dans le temps, le processus cognitif sous la forme d'une séquence d'états de connectivité fonctionnelle (fcSs : 'functional connectivity states'). Les résultats montrent qu'un traitement approprié du signal EEG permet d'identifier une dynamique spatio-temporelle dans les réseaux fonctionnels mis en jeu durant la tâche avec une excellente résolution temporelle (de l'ordre de la ms) et spatiale (~ 1000 régions d'intérêt). Cette dynamique correspond à une séquence de six fcSs (durée : 30 ms à 160 ms) caractérisés par une corrélation de phase significative des oscillations gamma (30-45 Hz). Des transitions rapides entre ces fcS sont observées et les réseaux associés à chaque fcS se recouvrent partiellement. Ces réseaux s'instancient sur des régions cérébrales pertinentes par rapport à la tâche de dénomination d'objets, depuis la perception de l'image jusqu'à l'articulation du nom. La méthode proposée ouvre de nombreuses perspectives quant à l'identification, à partir des données d'EEG de scalp, de réseaux cérébraux mis en jeu transitoirement lors d'activités cognitives. Abstract-The information processing in the human brain is a dynamic process that involves a rapid reorganization of functional brain networks, in a very short time scale (> number of electrodes), (ii) estimating the statistical dependencies (functional connectivity) between reconstructed sources (iii) characterizing the identified networks (in the form of nodes connected by edges forming a graph) by graph theory based analysis and (vi) segmenting, in time, the cognitive process as a sequence of functional connectivity states (fcSs). The results show that appropriate processing of the EEG signals can reveal the spatiotemporal dynamics of functional brain networks involved in the task with excellent temporal (on the order of ms) and spatial (~ 1000 regions of interest) resolution. This corresponds to a dynamic sequence of six fcSs (duration: 30 ms to 160 ms) with significant gamma phase synchronization (30-45 Hz). Rapid transitions between these fcS are observed and the networks associated with each fcS partially overlap. These networks disclose relevant brain regions related to picture naming task, from the perception of the image until the naming. The proposed method offers many opportunities in the identification, from the EEG data, of brain networks involved in cognitive activities

Spatiotemporal Analysis of Brain Functional Connectivity

International audienceBrain functions are based on interactions between neural assemblies distributed within and across distinct cerebral regions. During cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. In this context, the excellent temporal resolution (<1 ms) of the Electroencephalographic -EEG- signals allows for detection of very short-duration events and therefore, offers the unique opportunity to follow, over time, the dynamic properties of cognitive processes. In this paper we propose a new algorithm to track the functional brain connectivity dynamics. During picture recognition and naming task, this algorithm aims at segmenting high resolution (hr) EEG functional connectivity microstates. The proposed algorithm is based on the K-means clustering of the connectivity graphs obtained from the Phase Locking Values (PLV). Results show that the algorithm is able to track the brain functional connectivity dynamics during picture naming task

Methods for graph classification : application to the identification of neural cliques involved in memory porcesses

Le cerveau humain est un réseau «large-échelle» formé de régions corticales distribuées et fonctionnellement interconnectées. Le traitement de l'information par le cerveau est un processus dynamique mettant en jeu une réorganisation rapide des réseaux cérébraux fonctionnels, sur une échelle de temps très courte (inférieure à la seconde). Dans le champ des neurosciences cognitives, deux grandes questions restent ouvertes concernant ces réseaux. D'une part, est-il possible de suivre leur dynamique spatio-temporelle avec une résolution temporelle nettement supérieure à celle de l'IRM fonctionnelle? D'autre part, est-il possible de mettre en évidence des différences significatives dans ces réseaux lorsque le cerveau traite des stimuli (visuels, par exemple) ayant des caractéristiques différentes. Ces deux questions ont guidé les développements méthodologiques élaborés dans cette thèse. En effet, de nouvelles méthodes basées sur l'électroencéphalographie sont proposées. Ces méthodes permettent, d'une part de suivre la reconfiguration dynamique des réseaux cérébraux fonctionnels à une échelle de temps inférieure à la seconde. Elles permettent, d'autre part, de comparer deux réseaux cérébraux activés dans des conditions spécifiques. Nous proposons donc un nouvel algorithme bénéficiant de l'excellente résolution temporelle de l'EEG afin de suivre la reconfiguration rapide des réseaux fonctionnels cérébraux à l'échelle de la milliseconde. L'objectif principal de cet algorithme est de segmenter les réseaux cérébraux en un ensemble d' «états de connectivité fonctionnelle» à l'aide d'une approche de type « clustering ». L'algorithme est basé sur celui des K-means et a été appliqué sur les graphes de connectivité obtenus à partir de l'estimation des valeurs de connectivité fonctionnelle entre les régions d'intérêt considérées. La seconde question abordée dans ce travail relève de la mesure de similarité entre graphes. Ainsi, afin de comparer des réseaux de connectivité fonctionnelle, nous avons développé un algorithme (SimNet) capable de quantifier la similarité entre deux réseaux dont les nœuds sont définis spatialement. Cet algorithme met en correspondance les deux graphes en « déformant » le premier pour le rendre identique au second sur une contrainte de coût minimal associée à la déformation (insertion, suppression, substitution de nœuds et d’arêtes). Il procède selon deux étapes, la première consistant à calculer une distance sur les nœuds et la seconde une distance sur les arrêtes. Cet algorithme fournit un indice de similarité normalisé: 0 pour aucune similarité et 1 pour deux réseaux identiques. Il a été évalué sur des graphes simulés puis comparé à des algorithmes existants. Il montre de meilleures performances pour détecter la variation spatiale entre les graphes. Il a également été appliqué sur des données réelles afin de comparer différents réseaux cérébraux. Les résultats ont montré des performances élevées pour comparer deux réseaux cérébraux réels obtenus à partir l'EEG à haute résolution spatiale, au cours d'une tâche cognitive consistant à nommer des éléments de deux catégories différentes (objets vs animaux).The human brain is a "large-scale" network consisting of distributed and functionally interconnected regions. The information processing in the brain is a dynamic process that involves a fast reorganization of functional brain networks in a very short time scale (less than one second). In the field of cognitive neuroscience, two big questions remain about these networks. Firstly, is it possible to follow the spatiotemporal dynamics of the brain networks with a temporal resolution significantly higher than the functional MRI? Secondly, is it possible to detect a significant difference between these networks when the brain processes stimuli (visual, for example) with different characteristics? These two questions are the main motivations of this thesis. Indeed, we proposed new methods based on dense electroencephalography. These methods allow: i) to follow the dynamic reconfiguration of brain functional networks at millisecond time scale and ii) to compare two activated brain networks under specific conditions. We propose a new algorithm benefiting from the excellent temporal resolution of EEG to track the fast reconfiguration of the functional brain networks at millisecond time scale. The main objective of this algorithm is to segment the brain networks into a set of "functional connectivity states" using a network-clustering approach. The algorithm is based on K-means and was applied on the connectivity graphs obtained by estimation the functional connectivity values between the considered regions of interest. The second challenge addressed in this work falls within the measure of similarity between graphs. Thus, to compare functional connectivity networks, we developed an algorithm (SimNet) that able to quantify the similarity between two networks whose node coordinates is known. This algorithm maps one graph to the other using different operations (insertion, deletion, substitution of nodes and edges). The algorithm is based on two main parts, the first one is based on calculating the nodes distance and the second one is to calculate the edges distance. This algorithm provides a normalized similarity index: 0 for no similarity and 1 for two identical networks. SimNet was evaluated with simulated graphs and was compared with previously-published graph similarity algorithms. It shows high performance to detect the similarity variation between graphs involving a shifting of the location of nodes. It was also applied on real data to compare different brain networks. Results showed high performance in the comparison of real brain networks obtained from dense EEG during a cognitive task consisting in naming items of two different categories (objects vs. animals)

Brain network similarity: methods and applications

International audienceGraph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a &quot;network of networks&quot; that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications

Graph-based analysis of brain connectivity during spelling task

International audienceMost of the brain functions are based on interactions between neuronal assemblies distributed within and across distinct cerebral regions. A major challenge in neuroscience is to identify these networks from neuroimaging data. In this paper, we investigate the brain connectivity during a specific cognitive task: spelling the name of an object represented on a picture. By using high-resolution electroencephalography (hr-EEG) and phase synchrony analysis combined with graph theory-based analysis, we show that the topographic distribution of the phase synchrony and the graph parameters are very powerful tools to disclose the activated macro-regions involved in such a complex task

Identification of epileptogenic networks from dense EEG: A model-based study

International audienceEpilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r(2), h(2) and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMN

A new algorithm for spatiotemporal analysis of brain functional connectivity.

International audienceSpecific networks of interacting neuronal assemblies distributed within and across distinct brain regions underlie brain functions. In most cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. Among neuroimaging techniques, Magneto/Electroencephalography -M/EEG- allows for detection of very short-duration events and offers the single opportunity to follow, in time, the dynamic properties of cognitive processes(sub-millisecond temporal resolution). In this paper we propose a new algorithm to track the functional brain connectivity dynamics. During a picture naming task, this algorithm aims at segmenting high-resolution EEG signals (hr-EEG) into functional connectivity microstates. The proposed algorithm is based on the K-means clustering of the connectivity graphs obtained from the Phase Locking Value (PLV) method applied on hr-EEG. Results show that the analyzed evoked responses can be divided into six clusters representing distinct networks sequentially involved during the cognitive task, from the picture presentation and recognition to the motor response

SimiNet: a Novel Method for Quantifying Brain Network Similarity

International audienceQuantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, most of these algorithms ignore the physical location of the nodes, which is a key factor in the context of brain networks involving spatially defined functional areas. In this paper, we present a novel algorithm called “SimiNet” for measuring similarity between two graphs whose nodes are defined a priori within a 3D coordinate system. SimiNet provides a quantified index (ranging from 0 to 1) that accounts for node, edge and spatiality features. Complex graphs were simulated to evaluate the performance of SimiNet that is compared with eight state-of-art methods. Results show that SimiNet is able to detect weak spatial variations in compared graphs in addition to computing similarity using both nodes and edges. SimiNet was also applied to real brain networks obtained during a visual recognition task. The algorithm shows high performance to detect spatial variation of brain networks obtained during a naming task of two categories of visual stimuli: animals and tools. A perspective to this work is a better understanding of object categorization in the human brain