Graph Analysis of EEG Functional Connectivity Networks During a Letter-Speech Sound Binding Task in Adult Dyslexics

We performed an EEG graph analysis on data from 31 typical readers (22.27 ± 2.53 y/o) and 24 dyslexics (22.99 ± 2.29 y/o), recorded while they were engaged in an audiovisual task and during resting-state. The task simulates reading acquisition as participants learned new letter-sound mappings via feedback. EEG data was filtered for the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. We computed the Phase Lag Index (PLI) to provide an estimate of the functional connectivity between all pairs of electrodes per band. Then, networks were constructed using a Minimum Spanning Tree (MST), a unique sub-graph connecting all nodes (electrodes) without loops, aimed at minimizing bias in between groups and conditions comparisons. Both groups showed a comparable accuracy increase during task blocks, indicating that they correctly learned the new associations. The EEG results revealed lower task-specific theta connectivity, and lower theta degree correlation over both rest and task recordings, indicating less network integration in dyslexics compared to typical readers. This pattern suggests a role of theta oscillations in dyslexia and may reflect differences in task engagement between the groups, although robust correlations between MST metrics and performance indices were lacking.

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Corrigendum: Graph Analysis of EEG Functional Connectivity Networks During a Letter-Speech Sound Binding Task in Adult Dyslexics

Frontiers in Psychology ◽

10.3389/fpsyg.2021.828043 ◽

2022 ◽

Vol 12 ◽

Author(s):

Gorka Fraga-González ◽

Dirk J. A. Smit ◽

Melle J. W. Van der Molen ◽

Jurgen Tijms ◽

Cornelis J. Stam ◽

...

Keyword(s):

Functional Connectivity ◽

Speech Sound ◽

Graph Analysis ◽

Eeg Functional Connectivity

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Weighted Phase Lag Index and Graph Analysis: Preliminary Investigation of Functional Connectivity during Resting State in Children

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/186353 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 20

Author(s):

Erick Ortiz ◽

Krunoslav Stingl ◽

Jana Münßinger ◽

Christoph Braun ◽

Hubert Preissl ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Power Distribution ◽

Clustering Coefficient ◽

Graph Analysis ◽

Seed Region ◽

Phase Lag ◽

Alpha Band ◽

Occipital Area ◽

Weighted Phase Lag Index

Resting state functional connectivity of MEG data was studied in 29 children (9-10 years old). The weighted phase lag index (WPLI) was employed for estimating connectivity and compared to coherence. To further evaluate the network structure, a graph analysis based on WPLI was used to determine clustering coefficient (C) and betweenness centrality (BC) as local coefficients as well as the characteristic path length (L) as a parameter for global interconnectedness. The network’s modular structure was also calculated to estimate functional segregation. A seed region was identified in the central occipital area based on the power distribution at the sensor level in the alpha band. WPLI reveals a specific connectivity map different from power and coherence. BC and modularity show a strong level of connectedness in the occipital area between lateral and central sensors.Cshows different isolated areas of occipital sensors. Globally, a network with the shortestLis detected in the alpha band, consistently with the local results. Our results are in agreement with findings in adults, indicating a similar functional network in children at this age in the alpha band. The integrated use of WPLI and graph analysis can help to gain a better description of resting state networks.

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Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics

SLEEP ◽

10.1093/sleep/zsaa247 ◽

2020 ◽

Author(s):

Laura Sophie Imperatori ◽

Jacinthe Cataldi ◽

Monica Betta ◽

Emiliano Ricciardi ◽

Robin A A Ince ◽

...

Keyword(s):

Functional Connectivity ◽

Rem Sleep ◽

Phase Lag ◽

Linear Discriminant ◽

Functional Differences ◽

Combined Use ◽

Discriminant Analyses ◽

Eeg Functional Connectivity ◽

Weighted Phase Lag Index ◽

Healthy Participants

Abstract Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance—wakefulness (W), NREM-N2, NREM-N3, and REM sleep—with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5–4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12–16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.

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EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2018.00341 ◽

2018 ◽

Vol 12 ◽

Cited By ~ 6

Author(s):

Gorka Fraga González ◽

Dirk J. A. Smit ◽

Melle J. W. van der Molen ◽

Jurgen Tijms ◽

Cornelis Jan Stam ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Graph Analysis ◽

Phase Lag ◽

Resting State Functional Connectivity

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Sex Difference in EEG Functional Connectivity during Sleep Stages and Resting Wake State Based on Weighted Phase Lag Index

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2019.8857296 ◽

2019 ◽

Author(s):

Yuanyuan Liao ◽

Guolin Zhou ◽

Yu-xi Luo

Keyword(s):

Functional Connectivity ◽

Sex Difference ◽

Sleep Stages ◽

Phase Lag ◽

Eeg Functional Connectivity ◽

Weighted Phase Lag Index ◽

Wake State

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EEG functional connectivity metrics wPLI and wSMI account for d distinct types of brain functional interactions

10.1101/450270 ◽

2018 ◽

Author(s):

Laura Sophie Imperatori ◽

Monica Betta ◽

Luca Cecchetti ◽

André Canales Johnson ◽

Emiliano Ricciardi ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Connectivity ◽

Relative Increase ◽

Estimation Methods ◽

Phase Lag ◽

Neural Interactions ◽

Linear And Nonlinear ◽

Eeg Recordings ◽

Eeg Functional Connectivity ◽

Nonlinear Components

AbstractFunctional connectivity (FC) estimation methods are extensively used in neuroimaging to measure brain inter-regional interactions. The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent relatively robust exemplars of spectral (wPLI) and information-theoretic (wSMI) connectivity measures that recently gained increased popularity due to their relative immunity to volume conduction. wPLI and wSMI are posited to have different sensitivity to linear and nonlinear relationships between neural sources, but their performance has never been directly compared. Here, using simulated high-density (hd-)EEG data, we evaluated the accuracy of these two metrics for detecting distinct types of regional interdependencies characterised by different combinations of linear and nonlinear components. Our results demonstrate that while wPLI performs generally better at detecting functional couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI is able to detect exclusively nonlinear interaction dynamics. To evaluate the potential impact of these differences on real experimental data, we computed wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults obtained in wakefulness and deep (N3-)sleep. While both wPLI and wSMI revealed a relative decrease in alpha-connectivity during sleep relative to wakefulness, only wSMI identified a relative increase in theta-connectivity, while wPLI detected an increase in delta-connectivity, likely reflecting the occurrence of traveling slow waves. Overall, our findings indicate that wPLI and wSMI provide distinct but complementary information about functional brain connectivity, and that their combined use could advance our knowledge of neural interactions underlying different behavioural states.

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