Dynamic reshaping of functional brain networks during visual object recognition

Rizkallah, Jennifer; Benquet, Pascal; Kabbara, Aya; Dufor, Olivier; Wendling, Fabrice; Hassan, Mahmoud

doi:10.1088/1741-2552/aad7b1

Cited by 28 publications

(29 citation statements)

References 53 publications

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“…More essentially, scalp analysis does not allow making inferences about interacting brain regions. A potential solution is an emerging technique called "MEG/EEG source connectivity" (De Pasquale et al, 2010;Hipp et al, 2012;Mehrkanoon et al, 2014;Hassan et al, 2015;Kabbara et al, 2017;Mheich et al, 2017;Kabbara et al, 2018;Rizkallah et al, 2018), which reduces the aforementioned volume conduction. It is also conceptually attractive since networks can be directly identified at the cortical level with a high time/space resolution (for more details, see .…”

Section: Introductionmentioning

confidence: 99%

Decreased integration of EEG source-space networks in disorders of consciousness

Rizkallah

Annen

Modolo

et al. 2018

Preprint

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Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using highdensity electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks.Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS-(n=17) and UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate group differences between healthy volunteers and patient groups.Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness. Keywords: Disorders of consciousness, high-density electroencephalography, functionalbrain networks, unresponsive wakefulness syndrome, minimally conscious state.

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Section: Introductionmentioning

confidence: 99%

Decreased integration of EEG source-space networks in disorders of consciousness

Rizkallah

Annen

Modolo

et al. 2018

Preprint

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“…An atlas-based approach was used to project EEG signals onto an anatomical framework consisting of 68 cortical regions identified by means of the Desikan-Killiany atlas (Desikan et al, 2006). To reconstruct the regional time series, we used the weighted Minimum Norm Estimate (wMNE), widely used in the context of EEG source localization (Gramfort et al, 2012;Hassan et al, 2015;Hauk, 2004;Kabbara et al, 2017;Rizkallah et al, 2018) and showed higher performance than other algorithms in several comparative studies (Hassan et al, 2014;Hassan et al, 2016). The regional time series were then filtered in the different EEG frequency bands: Delta [0.5-4 Hz], Theta [4][5][6][7][8], alpha [8][9][10][11][12][13], beta [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] and gamma [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]…”

Section: Estimation Of Regional Time Seriesmentioning

confidence: 99%

Removing zero-lag functional connections can alter EEG-source space networks at rest

Rizkallah

Amoud

Fraschini

et al. 2019

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Electro-encephalography (EEG) source connectivity is an emerging approach to estimate brain networks with high time/space resolution. Here, we aim to evaluate the effect of different functional connectivity (FC) methods on the EEG-source space networks at rest. The two main families of FC methods tested are: i) the FC methods that do not remove the zerolag connectivity including the Phase Locking Value (PLV) and the Amplitude Envelope Correlation (AEC) and ii) the FC methods that remove the zero-lag connections such as the Phase Lag Index (PLI) and orthogonalisation approach combined with PLV (PLVorth) and AEC (AECorth). Methods are evaluated on resting state dense-EEG signals recorded from 20 healthy participants. Networks obtained by each FC method are compared with fMRI networks at rest (from the Human Connectome Project -HCP-, N=487). Results show low correlations for all the FC methods, however PLV and AEC networks are significantly correlated with fMRI networks (ρ = 0.12, p = 1.9310 -8 and ρ = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for each EEG frequency bands and for different FC matrices threshold. Furthermore, the effect of electrode density was also tested using four EEG montages (dense-EEG 256 electrodes, 128, 64 and 32 electrodes).Results show no significant differences between the four EEG montages in terms of correlations with the fMRI networks. Our main message here is to be careful when selecting the FC methods and mainly those that remove the zero-lag connections as they can affect the network characteristics. More comparative studies (based on simulation and real data) are still needed in order to make EEG source connectivity a mature technique to address questions in cognitive and clinical neuroscience.

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“…Electrode impedances were kept below 20 kOhm. EEG signals are frequently contaminated by several sources of artifacts, which were addressed using the same preprocessing steps as described in several previous studies dealing with EEG resting-state data (Kabbara et al, 2017;Kabbara et al, 2018;Rizkallah et al, 2018). Briefly, bad channels (signals that are either completely flat or contaminated by movement artifacts) were identified by visual inspection, complemented by the power spectral density.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…Dynamic brain networks were reconstructed using the "EEG source connectivity" method , combined with a sliding window approach as detailed in (Kabbara et al, 2017;Kabbara et al, 2018;Rizkallah et al, 2018). "EEG source connectivity" involves two main steps: i) solving the EEG inverse problem in order to estimate the cortical sources and reconstruct their temporal dynamics, and ii) measuring the functional connectivity between the reconstructed time-series.…”

Section: Dynamic Brain Network Constructionmentioning

confidence: 99%

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Brain network dynamics correlate with personality traits

Kabbara

Paban

Weill

et al. 2019

Preprint

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Identifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personalityrelated differences in functional connectivity has been obtained through the stationary analysis, which do not capture the complex dynamical properties of brain networks. In this study, we aimed to evaluate the feasibility of dynamic network measures to predict personality traits. Using the EEGsource connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from EEG data acquired from 45 subjects during resting state. Then, several dynamic functional connectivity metrics were evaluated. Results showed a negative correlation between neuroticism and the dynamic variability of temporal lobe regions in terms of strength. In addition, a positive correlation was found between agreeableness and centrality variability of the posterior cingulate, which is known as a key hub in resting state networks. Results also revealed that extraversion is positively correlated with the dynamics of the superior parietal region. These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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Dynamic reshaping of functional brain networks during visual object recognition

Abstract: We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Cited by 28 publications

References 53 publications

Decreased integration of EEG source-space networks in disorders of consciousness

Decreased integration of EEG source-space networks in disorders of consciousness

Removing zero-lag functional connections can alter EEG-source space networks at rest

Brain network dynamics correlate with personality traits

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