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

Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style

Neuropsychological data about the forms of acquired reading impairment provide a strong basis for the theoretical framework of the dual-route cascade (DRC) model which is predictive of reading performance. However, lesions are often extensive and heterogeneous, thus making it difficult to establish precise functional anatomical correlates. Here, we provide a connective neural account in the aim of accommodating the main principles of the DRC framework and to make predictions on reading skill. We located prominent reading areas using fMRI and applied structural equation modeling to pinpoint distinct neural pathways. Functionality of regions together with neural network dissociations between words and pseudowords corroborate the existing neuroanatomical view on the DRC and provide a novel outlook on the sub-regions involved. In a similar vein, congruent (or incongruent) reliance of pathways, that is reliance on the word (or pseudoword) pathway during word reading and on the pseudoword (or word) pathway during pseudoword reading predicted good (or poor) reading performance as assessed by out-of-magnet reading tests. Finally, inter-individual analysis unraveled an efficient reading style mirroring pathway reliance as a function of the fingerprint of the stimulus to be read, suggesting an optimal pattern of cerebral information trafficking which leads to high reading performance

Left premotor cortex and allophonic speech perception in dyslexia: a PET study.

International audienceDisorders of categorical perception has been put forward as a new account of phonological deficit in dyslexia (Serniclaes, W., Sprenger-Charolles, L., Carre, R. and Demonet, J.F., 2001. Perceptual discrimination of speech sounds in developmental dyslexia. J. Speech Lang. Hear. Res. 44, 384-399.) so that dyslexic subjects tend to discriminate phoneme instances within a given phonemic category rather than between categories, possibly witnessing the persistence of phonemic boundaries of 'allophones' that may be relevant to other languages although not to one's mother tongue (Serniclaes, W., Van Heghe, S., Mousty, P., Carre, R. and Sprenger-Charolles, L., 2004. Allophonic mode of speech perception in dyslexia. J. Exp. Child Psychol. 87, 336-361.). The brain correlates of within- and between-category discrimination were explored using a /ba/-/da/ phonetic continuum and H(2)(15)O PET in 14 dyslexic and 16 control adult readers; subjects discriminated a set of stimuli pairs, first in a 'naïve' (acoustic) condition and, after debriefing about the stimuli identity, in a speech (phonemic) condition (Dufor, O., Serniclaes, W., Sprenger-Charolles, L. and Demonet, J.F., 2007. Top-down processes during auditory phoneme categorization in dyslexia: a PET study. NeuroImage 34, 1692-1707.). While discrimination of 'between' pairs improved in all subjects following debriefing, 'within' stimuli yielded variable performance; some subjects kept discriminating them, while best categorizers judged them identical. Correlation analyses between acoustic-to-speech changes in brain activity and in 'within'-pair discrimination, and between control and dyslexic groups, revealed a criss-crossed correlation pattern in the left BA6 so that the higher the activity the better the categorization in control subjects whereas the higher the activity the more increased 'within' discrimination in dyslexic subjects. Therefore, in average readers, enhanced activity in the left BA6 likely contributes to optimizing phoneme categorization via refined speech motor coding. In dyslexic subjects showing sensitivity to 'within'-category cues, activity enhancement in this region might suggest the persistence of motor coding for allophonic representations of speech

Top-down processes during auditory phoneme categorization in dyslexia: a PET study.

International audienceWhile persistence of subtle phonological deficits in dyslexic adults is well documented, deficit of categorical perception of phonemes has received little attention so far. We studied learning of phoneme categorization during an activation H(2)O(15) PET experiment in 14 dyslexic adults and 16 normal readers with similar age, handedness and performance IQ. Dyslexic subjects exhibited typical, marked impairments in reading and phoneme awareness tasks. During the PET experiment, subjects performed a discrimination task involving sine wave analogues of speech first presented as pairs of electronic sounds and, after debriefing, as syllables /ba/ and /da/. Discrimination performance and brain activation were compared between the acoustic mode and the speech mode of the task which involved physically identical stimuli; signal changes in the speech mode relative to the acoustic mode revealed the neural counterparts of phonological top-down processes that are engaged after debriefing. Although dyslexic subjects showed good abilities to learn discriminating speech sounds, their performance remained lower than those of normal readers on the discrimination task over the whole experiment. Activation observed in the speech mode in normal readers showed a strongly left-lateralized pattern involving the superior temporal, inferior parietal and inferior lateral frontal cortex. Frontal and parietal subparts of these left-sided regions were significantly more activated in the control group than in the dyslexic group. Activations in the right frontal cortex were larger in the dyslexic group than in the control group for both speech and acoustic modes relative to rest. Dyslexic subjects showed an unexpected large deactivation in the medial occipital cortex for the acoustic mode that may reflect increased effortful attention to auditory stimuli

Itch Matrixes

International audienc

Dynamic reshaping of functional brain networks during visual object recognition

International audienc

HD-EEG for tracking sub-second brain dynamics during cognitive tasks

International audienceThis work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org

A novel algorithm for measuring graph similarity: application to brain networks

International audiencemeasuring similarity among graphs is a challenging issue in many disciplines including neuroscience. Several algorithms, mainly based on vertices or edges properties, were proposed to address this issue. Most of them ignore the physical location of the vertices, which is a crucial factor in the analysis of brain networks. Indeed, functional brain networks are usually represented as graphs composed of vertices (brain regions) connected by edges (functional connectivity). In this paper, we propose a novel algorithm to measure a similarity between graphs. The novelty of our approach is to account for vertices, edges and spatiality at the same time. The proposed algorithm is evaluated using synthetic graphs. It shows high ability to detect and measure similarity between graphs. An application to real functional brain networks is then described. The algorithm allows for quantification of the inter-subjects variability during a picture naming task