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Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses

ABSTRACT: Highlights • The irritative zone differs on Electrical Source Imaging (ESI) and EEG-fMRI.• Findings differ in functional connectivity and show low temporal correlation.• ESI and EEG-fMRI reveal distinct aspects of the irritative zone.• The differences may reflect differences in temporal resolution of the techniques. Summary Objective The irritative zone - the area generating epileptic spikes - can be studied non-invasively during the interictal period using Electrical Source Imaging (ESI) and simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI). Although the techniques yield results which may overlap spatially, differences in spatial localization of the irritative zone within the same patient are consistently observed. To investigate this discrepancy, we used Blood Oxygenation Level Dependent (BOLD) functional connectivity measures to examine the underlying relationship between ESI and EEG-fMRI findings. Methods Fifteen patients (age 20–54), who underwent presurgical epilepsy investigation, were scanned using a single-session resting-state EEG-fMRI protocol. Structural MRI was used to obtain the electrode localisation of a high-density 64-channel EEG cap. Electrical generators of interictal epileptiform discharges were obtained using a distributed local autoregressive average (LAURA) algorithm as implemented in Cartool EEG software. BOLD activations were obtained using both spike-related and voltage-map EEG-fMRI analysis. The global maxima of each method were used to investigate the temporal relationship of BOLD time courses and to assess the spatial similarity using the Dice similarity index between functional connectivity maps. Results ESI, voltage-map and spike-related EEG-fMRI methods identified peaks in 15 (100%), 13 (67%) and 8 (53%) of the 15 patients, respectively. For all methods, maxima were localised within the same lobe, but differed in sub-lobar localisation, with a median distance of 22.8 mm between the highest peak for each method. The functional connectivity analysis showed that the temporal correlation between maxima only explained 38% of the variance between the time course of the BOLD response at the maxima. The mean Dice similarity index between seed-voxel functional connectivity maps showed poor spatial agreement. Significance Non-invasive methods for the localisation of the irritative zone have distinct spatial and temporal sensitivity to different aspects of the local cortical network involved in the generation of interictal epileptiform discharges.

SUBMITTER: Urriola J

PROVIDER: S-EPMC7527619 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:Objective:Electrical source imaging (ESI) is used increasingly to estimate the epileptogenic zone (EZ) in patients with epilepsy. Directed functional connectivity (DFC) coupled to ESI helps to better characterize epileptic networks, but studies on interictal activity have relied on high-density recordings. We investigated the accuracy of ESI and DFC for localizing the EZ, based on low-density clinical electroencephalography (EEG). Methods:We selected patients with the following: (a) focal epilepsy, (b) interictal spikes on standard EEG, (c) either a focal structural lesion concordant with the electroclinical semiology or good postoperative outcome. In 34 patients (20 temporal lobe epilepsy [TLE], 14 extra-TLE [ETLE]), we marked interictal spikes and estimated the cortical activity during each spike in 82 cortical regions using a patient-specific head model and distributed linear inverse solution. DFC between brain regions was computed using Granger-causal modeling followed by network topologic measures. The concordance with the presumed EZ at the sublobar level was computed using the epileptogenic lesion or the resected area in postoperative seizure-free patients. Results:ESI, summed outflow, and efficiency were concordant with the presumed EZ in 76% of the patients, whereas the clustering coefficient and betweenness centrality were concordant in 70% of patients. There was no significant difference between ESI and connectivity measures. In all measures, patients with TLE had a significantly higher (P < 0.05) concordance with the presumed EZ than patients with with ETLE. The brain volume accepted for concordance was significantly larger in TLE. Significance:ESI and DFC derived from low-density EEG can reliably estimate the EZ from interictal spikes. Connectivity measures were not superior to ESI for EZ localization during interictal spikes, but the current validation of the localization of connectivity measure is promising for other applications.

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Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses

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