Perception and comprehension of linguistic and affective prosody in children with Landau-Kleffner syndrome

International audienceThe present study investigated language outcomes in children with Landau-Kleffner syndrome compared with 7 to 8 year-old healthy children and healthy adults. We examined their capacity of understanding simple sentences using linguistic and affective prosodic cues and perceiving them. A battery of prosodic tests was elaborated and used for this study. Results revealed certain delayed language development or a different pattern of performance in participants with Landau-Kleffner syndrome. With more subjects tested in the future results from our battery of prosodic tests would allow us to better understand language development in child and it would be helpful for speech-language therapies

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

Minimum information required for written word recognition

International audienceReading is an automated process. One of the remarkable human abilities is that we can read even partially erased or hidden words. We carried out a study on written word recognition in order to decipher how much information is required at least to identify a word. Experimental software was designed in C++ language to measure the amount of information in pixels and reaction time. The results showed we could identify words at a very low display rate and suggest that prior knowledge on the category of words play a mediating role in written word recognition

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

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

Striatal Synapse Degeneration and Dysfunction Are Reversed by Reactivation of Wnt Signaling

Synapse degeneration in the striatum has been associated with the early stages of Parkinson’s and Huntington’s diseases (PD and HD). However, the molecular mechanisms that trigger synaptic dysfunction and loss are not fully understood. Increasing evidence suggests that deficiency in Wnt signaling triggers synapse degeneration in the adult brain and that this pathway is affected in neurodegenerative diseases. Here, we demonstrate that endogenous Wnt signaling is essential for the integrity of a subset of inhibitory synapses on striatal medium spiny neurons (MSNs). We found that inducible expression of the specific Wnt antagonist Dickkopf-1 (Dkk1) in the adult striatum leads to the loss of inhibitory synapses on MSNs and affects the synaptic transmission of D2-MSNs. We also discovered that re-activation of the Wnt pathway by turning off Dkk1 expression after substantial loss of synapses resulted in the complete recovery of GABAergic and dopamine synapse number. Our results also show that re-activation of the Wnt pathway leads to a recovery of amphetamine response and motor function. Our studies identify the Wnt signaling pathway as a potential therapeutic target for restoring neuronal circuits after synapse degeneration

Brain network modules of meaningful and meaningless objects

Network modularity is a key feature for efficient information processing in the human brain. This information processing is however dynamic and networks can reconfigure at very short time period, few hundreds of millisecond. This requires neuroimaging techniques with sufficient time resolution. Here we use the dense electroencephalography, EEG, source connectivity methods to identify cortical networks with excellent time resolution, in the order of millisecond. We identify functional networks during picture naming task. Two categories of visual stimuli were presented, meaningful (tools, animals) and meaningless (scrambled) objects. In this paper, we report the reconfiguration of brain network modularity for meaningful and meaningless objects. Results showed mainly that networks of meaningful objects were more modular than those of meaningless objects. Networks of the ventral visual pathway were activated in both cases. However a strong occipitotemporal functional connectivity appeared for meaningful object but not for meaningless object. We believe that this approach will give new insights into the dynamic behavior of the brain networks during fast information processing.Comment: The 3rd Middle East Conference on Biomedical Engineering (MECBME'16