The dynamic functional core network of the human brain at rest

Kabbara, Aya; Falou, Wassim El; Khalil, Mohamad; Wendling, Fabrice; Hassan, Mahmoud

doi:10.1038/s41598-017-03420-6

Cited by 130 publications

(146 citation statements)

References 91 publications

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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%

“…Five artifactfree epochs of 40-s lengths were selected for each participant. This epoch length was used in a previous study, and was considered as a good compromise between the needed temporal resolution and the results reproducibility (Kabbara et al, 2017).…”

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%

“…On the other side, emerging evidence show that most cognitive states and behavioral functions depend on the activity of numerous brain regions operating as a large-scale network (Bressler, 1995;Edelman, 1993;Fuster, 2010;Goldman-Rakic, 1988;Greicius et al, 2003;Mesulam, 1990;Sporns et al, 2004). This dynamical behavior is even present in the pattern of intrinsic or spontaneous brain activity (i.e., when the person is at rest) (Allen et al, 2014;Baker et al, 2014;de Pasquale et al, 2015de Pasquale et al, , 2012Kabbara et al, 2017;O'Neill et al, 2017). In particular, the dynamics of brain connectivity patterns can be studied at the millisecond time scale, for example using electro-encephalography (EEG).…”

Section: Introductionmentioning

confidence: 99%

“…Here, we collected EEG data of 45 subjects during resting state. Dynamic brain networks were reconstructed using the EEG source connectivity approach combined with a sliding window approach as in (Kabbara et al, 2017;Rizkallah et al, 2018). Then, based on graph theoretical approaches, several dynamic features were estimated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Section: Dynamic Brain Network Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Brain network dynamics correlate with personality traits

Kabbara

Paban

Weill

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

Connectivity within regions characterizes epilepsy duration and treatment outcome

Xue

Wang

Kopetzky

et al. 2021

Human Brain Mapping

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Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network ($50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution ($50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan-Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions).Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.

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Investigating the spectral features of the brain meso‐scale structure at rest

Iandolo

Semprini

Sona

et al. 2021

Human Brain Mapping

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Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized highdensity electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of mesoscale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30-50 Hz). We further described other features of the meso-scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and coreperipheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity-invariance). In conclusion, we observed that the brain spontaneous activity shows frequency-specific meso-scale organization, which may support spatially distributed and local information processing.

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The dynamic functional core network of the human brain at rest

Cited by 130 publications

References 91 publications

Brain network dynamics correlate with personality traits

Brain network dynamics correlate with personality traits

Connectivity within regions characterizes epilepsy duration and treatment outcome

Investigating the spectral features of the brain meso‐scale structure at rest

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