Dynamic reorganization of functional brain networks during picture naming

Abstract

For 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.

The dynamic functional core network of the human brain at rest

“…This method was recently evaluated for its capacity to reveal relevant networks in the context of cognitive tasks 44 and brain disorders 62 . It was then extended to track the spatiotemporal dynamics of functional brain networks 43 . More recently, we have performed a preliminary study using this technique combined with graph theory to explore the brain network architecture during rest in a static way 32 .…”

“…We then reconstructed the functional networks using EEG source connectivity approach as described in previous work 32, 43, 44 . Topologies of the identified networks were characterized in terms of node’s strength, vulnerability, betweenness centrality and clustering coefficient.…”

Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease

“…Here, EEG source connectivity approach was used to identify functional networks at the cortical level from scalp dense-EEG recordings. This method was first evaluated for its capacity to reveal relevant networks in a picture naming task (Hassan et al, 2014) and was then extended to the tracking of the spatiotemporal dynamics of reconstructed brain networks (Hassan et al, 2015a, Hassan and Wendling, 2015). Graph theory metrics were firstly computed reflecting the global topology characteristics of the network.…”

“…Our main objective was to detect alterations in functional networks according to the severity of cognitive impairment; essentially those related to mild cognitive deficits, which represent a serious challenge nowadays. To do so, functional connectivity was investigated using a ‘EEG source connectivity’ method (Hassan et al, 2015a, Hassan et al, 2014). As compared with fMRI studies of functional connectivity, a unique advantage of this method is that networks could be directly identified at the cerebral cortex level from scalp EEG recordings, which consist in direct measurement of neuronal activity, in contrast with blood-oxygen-level-dependent (BOLD) signals.…”

Brain network modules of meaningful and meaningless objects

“…Functional connectivity matrices were calculated using method called 'EEG source connectivity' [13,14]. It includes two main steps: i) solving the EEG inverse problem to reconstruct regional time series and ii) measuring the statistical couplings, functional connectivity, between these reconstructed regional time series.…”