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

Le De hominis miseria, mundi et inferni contemptu de Hugues de Miramar, une œuvre ‘autobiographique’ dans la postérité des Confessions d’Augustin ?

La question de l’existence d’œuvres ‘autobiographiques’ au Moyen Age a fait l’objet de nombreuses études, qui ne mentionnent jamais le De hominis miseria, mundi et inferni contemptu de Hugues de Miramar. Or, malgré son titre et son contenu (celui d’un traité sur le thème classique du contemptus mundi), cet ouvrage, au-delà de deux passages clairement ‘autobiographiques’, insère dans sa structure d’ensemble, celle d’un traité monastique, un récit rétrospectif à la première personne. Dans son écriture, le De miseria se rattache ainsi, dans une certaine mesure, à la postérité des Confessions de saint Augustin. A cet égard, sa place dans l’histoire littéraire du Moyen Age mériterait d’être reconnue.The question of the existence of ‘autobiographical’ works in the Middle Ages has been dealt with in numerous studies, which never mention the De hominis miseria, mundi and inferni contemptu by Hugues de Miramar. Now, in spite of its title and its contents (that of a treatise on the classical theme of contemptus mundi), this work, beyond two passages clearly ‘autobiographical’, inserts into its general structure, that of a monastic treatise, a retrospective first-person narrative. Thus, in its writing, the De miseria, to some extent, is connected with the posterity of St. Augustine’ s Confessions.In this respect, its place in the Middle Ages’ literary history would deserve to be recognized

Model-based measurement of epileptic tissue excitability.

International audienceIn the context of pre-surgical evaluation of epileptic patients, depth-EEG signals constitute a valuable source of information to characterize the spatiotemporal organization of paroxysmal interictal and ictal activities, prior to surgery. However, interpretation of these very complex data remains a formidable task. Indeed, interpretation is currently mostly qualitative and efforts are still to be produced in order to quantitatively assess pathophysiological information conveyed by signals. The proposed EEG model-based approach is a contribution to this effort. It introduces both a physiological parameter set which represents excitation and inhibition levels in recorded neuronal tissue and a methodology to estimate this set of parameters. It includes Sequential Monte Carlo nonlinear filtering to estimate hidden state trajectory from EEG and Particle Swarm Optimization to maximize a likelihood function deduced from Monte Carlo computations. Simulation results illustrate what it can be expected from this methodology

Localization of extended brain sources from EEG/MEG: The ExSo-MUSIC approach.

International audienceWe propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q≥1). This method is an extension of the 2q-MUSIC (q≥1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q≥2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms

Realistic modeling of entorhinal cortex field potentials and interpretation of epileptic activity in the guinea pig isolated brain preparation.

Mechanisms underlying epileptic activities recorded from entorhinal cortex (EC) were studied through a computational model based on review of cytoarchitectonic and neurobiological data about this structure. The purpose of this study is to describe and use this model to interpret epileptiform discharge patterns recorded in an experimental model of ictogenesis (guinea-pig isolated brain perfused with bicuculline). A macroscopic modeling approach representing synaptic interactions between cells subpopulations in the EC was chosen for its adequacy to mimic field potentials reflecting overall dynamics rising from interconnected cells populations. Therefore, intrinsic properties of neurons were not included in the modeling design. Model parameters were adjusted from an identification procedure based on quantitative comparison between real and simulated signals. For both EC deep and superficial layers, results show that the model generates very realistic signals regarding temporal dynamics, spectral features and cross-correlation values. These simulations allowed us to infer information about the evolution of synaptic transmission between principal cell and interneuronal populations and about connectivity between deep and superficial layers during the transition from background to ictal activity. In the model, this transition was obtained for increased excitation in deep versus superficial layers. Transitions between epileptiform activities (interictal spikes, fast onset activity (25Hz), ictal bursting activity) were explained by changes of parameters mainly related to GABAergic interactions. Notably, the model predicted an important role of GABA(a,fast) and GABA(b) receptor-mediated inhibition in the generation of ictal fast onset and burst activities, respectively. These findings are discussed with respect to experimental data

Complex dynamics for the study of neural activity in the human brain

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).National audienceNeural mass modeling is a part of computational neuroscience that was developed to study the general behavior of interacting neuronal populations. This type of mesoscopic model is able to generate output signals that are comparable with experimental data such as electroencephalograms. Classically, neural mass models consider two interconnected populations. One interaction have been modeled in two differents ways. In this work we propose and analyze a neural mass model embedding both approaches and compare the generated time series to real data

Dynamic reorganization of functional brain networks during picture naming.

International audienceFor efficient information processing during cognitive activity, functional brain networks have to rapidly and dynamically reorganize on a sub-second time scale. Tracking the spatiotemporal dynamics of large scale networks over this short time duration is a very challenging issue. Here, we tackle this problem by using dense electroencephalography (EEG) recorded during a picture naming task. We found that (i) the picture naming task can be divided into six brain network states (BNSs) characterized by significantly high synchronization of gamma (30–45 Hz) oscillations, (ii) fast transitions occur between these BNSs that last from 30 msec to 160 msec, (iii) based on the state of the art of the picture naming task, we consider that the spatial location of their nodes and edges, as well as the timing of transitions, indicate that each network can be associated with one or several specific function (from visual processing to articulation) and (iv) the comparison with previously-used approach aimed at localizing the sources showed that the network-based approach reveals networks that are more specific to the performed task. We speculate that the persistence of several brain regions in successive BNSs participates to fast and efficient information processing in the brain