Mapping Epileptic Networks Using Simultaneous Intracranial EEG-fMRI

Chaudhary, Umair J.; Centeno, María; Carmichael, David W.; Diehl, Beate; Walker, Matthew C.; Duncan, John S.; Lemieux, Louis

doi:10.3389/fneur.2021.693504

Cited by 6 publications

(3 citation statements)

References 71 publications

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“…However, for the chosen reconstruction method their results were only partially validated, i.e., only few patients or regions presented concordant results with both techniques. Although simultaneous icEEG-fMRI can overcome the low sensitivity of scalp EEG and reach good spatial concordance between neuronal electrical and BOLD changes, it comes at the cost of invasive recordings only affordable in specific cases (Vulliemoz et al 2011;Cunningham et al 2012;Aghakhani et al 2015;Chaudhary et al 2016Chaudhary et al , 2021Sharma et al 2019). Furthermore, these studies did not assess the quality of different source reconstruction algorithms and the effect on concordance measures, which stresses the need to perform systematic comparisons focusing on specific scenarios and considering other less explored methodologies.…”

Section: Introductionmentioning

confidence: 99%

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors

et al. 2022

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Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.

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Section: Introductionmentioning

confidence: 99%

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors

et al. 2022

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“…Current FC studies focus primarily on the analysis of resting-state iEEG recordings, which show synchronous neural activity in different regions in the absence of any task-related activity 17 , 18 . Most iEEG studies examined FC measures during either ictal or interictal states 7 , 19 .…”

Section: Introductionmentioning

confidence: 99%

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Rijal

Corona

Perry

et al. 2023

Sci Rep

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Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes’ nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II–IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47–100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.

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“…Current FC studies focus primarily on the analysis of resting-state iEEG recordings, which show synchronous neural activity in different brain regions in the absence of any task-related activity 14,15 . Yet, there are no studies so far assessing changes in FC measures across different epileptogenic states.…”

Section: Introductionmentioning

confidence: 99%

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Rijal

Corona

Perry

et al. 2022

Preprint

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Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Nodes of these networks are the target of epilepsy surgery. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography (iEEG) can quantify epileptogenicity and predict surgical outcome in children with drug-resistant epilepsy (DRE). We computed Amplitude Envelope Correlation (AEC) and Phase Locking Value (PLV) for different states (i.e., interictal with no spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and for different frequency bands. We then computed each node’s strength (i.e., AEC or PLV at iEEG electrodes). We observed differences in nodal strength among the different states following a hierarchical epileptogenic organization: lower FC in interictal and pre-ictal states followed by higher FC values in ictal and post-ictal states (p < 0.05). We also observed higher nodal strength within resection for patients with good outcome (n = 22, Engel I), but not for poor outcome (n = 9, Engel II-IV), for all states (except ictal) and all bands (p < 0.05). Resection of hubs with high nodal strength was predictive of outcome (75–92% positive and 47–63% negative predictive values). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in children with DRE.

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Mapping Epileptic Networks Using Simultaneous Intracranial EEG-fMRI

Cited by 6 publications

References 71 publications

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

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