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

González, Gorka Fraga; Smit, Dirk J.A.; Molen, Melle J. W. van der; Tijms, Jurgen; Stam, Cornelis J.; Geus, Eco J. C. de; Molen, Maurits W. van der

doi:10.3389/fnhum.2018.00341

Cited by 61 publications

(42 citation statements)

References 92 publications

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“…The considered parameter configurations resulted in a low activity state of disconnected nodes (G = 0) and generation of limit-cycle oscillations with an alpha-band frequency when the individual regions were coupled (G > 0). The modelled alpha oscillations have been shown to be dominant in EEG of human resting-state brain activity (Fraga González et al, 2018;Spitoni, Di Russo, Cimmino, Bozzacchi, & Pizzamiglio, 2013) and to interact with BOLD responses (Mayhew, Ostwald, Porcaro, & Bagshaw, 2013).…”

Section: Neural Mass Modelmentioning

confidence: 99%

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Domhof

Jung

Eickhoff

et al. 2021

Network Neuroscience

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Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modelling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modelling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modelling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models toempirical data at a global group but not a single-subject level, which provides further insights into the personalisation of whole-brain models.

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Section: Neural Mass Modelmentioning

confidence: 99%

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Domhof

Jung

Eickhoff

et al. 2021

Network Neuroscience

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“…Moreover the reduced connectivity between visual association areas and prefrontal attention areas during tasks may indicate worse integration and modulation of attentional control to visual information in impaired readers ( Finn et al, 2014 ). To summarize, functional connectivity literature suggests broader connectivity deviances in dyslexia that extend beyond the language networks (e.g., Wolf et al, 2010 ; Finn et al, 2014 ; Fraga González et al, 2016 , 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Development of Print-Speech Integration in the Brain of Beginning Readers With Varying Reading Skills

Wang

Karipidis

Pleisch

et al. 2020

Front. Hum. Neurosci.

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Wang et al. Development of Print-Speech Integration in Beginning Readers RESEARCH HIGHLIGHTS-Differential processing of congruent and incongruent audiovisual nonwords emerges in second grade and is reflected by an incongruency effect in right MTG/ITG.-Pseudoword reading improvements with time are associated with the strength of the emerging congruency effect to audiovisual nonwords in the left STG.-Functional coupling between the left occipito-temporal and the right SPL increases in typical readers for congruent pairs from first to second grade.-Functional coupling between the left occipito-temporal and the left IFG/STG increases in poor readers for incongruent pairs from first to second grade.

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“…In the Fourier domain, spectral characteristics of the standard EEG frequency sub-bands, namely delta (δ) (0.5-4 Hz), theta (θ) (4-8 Hz), alpha (α) (8)(9)(10)(11)(12)(13) and beta (β) (13-30 Hz) have been extensively used as input features for the different interictal spike detection and classification algorithms such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) [14]- [20]. We note that in this study as well as others [21], the gamma sub-band (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) was excluded due to the presence of evidence suggesting that the gamma frequency range in scalp EEG recordings may be strongly affected by muscle artifact [22]. In addition, [23] reported that the gamma sub-band yields low reliability graph metrics.…”

Section: Introductionmentioning

confidence: 84%

Penalized Functional Connectivity Maps for Patients With Focal Epilepsy

et al. 2021

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Objective: This study demonstrates how functional connectivity (FC) patterns are affected in direct relation to the lobe that is mostly affected by seizures. Methods: The novel idea of penalized FC (pFC) maps is compared against standard FC maps in the four fundamental EEG frequency sub-bands. The FC measure between any two specific electrodes is scaled depending on the probability of true FC between them, and their power content with respect to the two electrodes of maximum power within each frequency sub-band. The algorithm is automated and introduces adaptive power penalization based on the power distribution of the different sub-bands. Results: The pFC maps were found to be more effective at suppressing the local connectivity in the lobes that are less affected by the interictal epileptiform discharges (IEDs). More precisely, given the least amount of power penalization, pFC maps of the theta sub-band reveal statistical significance in terms of increased local connectivity margin of the affected region as compared to the standard FC maps. However, they cannot be solely relied upon as other sub-bands could alternatively show high local connectivity across different patients within the region of interest. Conclusion: penalized functional connectivity maps intrinsically provide more information regarding the whole brain network in context to regions of interest where the active lobe is determined by the neurologists to contain the focal source. Significance: Findings suggest that (1) the significant sub-band varies from patient to patient while remaining relatively consistent within the IED segments of a same patient, and (2) the pFC maps have an advanced capability in terms of pinpointing to a region of interest of the active lobe, and as such can play a critical role in providing insight as to a region of interest where the 3D source might be located when solving the ill-posed inverse problem.INDEX TERMS Focal epilepsy, Interictal Epileptiform Discharges (IEDs), penalized Functional Connectivity (pFC), penalized Weighted Phase Lag Index (pWPLI).

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

Cited by 61 publications

References 92 publications

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Development of Print-Speech Integration in the Brain of Beginning Readers With Varying Reading Skills

Penalized Functional Connectivity Maps for Patients With Focal Epilepsy

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