Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity

Mierlo, Pieter van; Höller, Yvonne; Focke, Niels K.; Vulliémoz, Serge

doi:10.3389/fneur.2019.00721

Cited by 57 publications

(45 citation statements)

References 101 publications

(130 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Englot et al ( 10 ) reported increased connectivity in the resected region to be more frequent in seizure-free patients. Our findings support a strong involvement of hub nodes in epileptogenic networks ( 14 ).…”

Section: Discussionsupporting

confidence: 85%

See 1 more Smart Citation

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

et al. 2020

View full text Add to dashboard Cite

Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.

show abstract

Section: Discussionsupporting

confidence: 85%

“…We hypothesized that the removal of high strength nodes would result in seizure-freedom. Our findings support earlier literature of being strong involvement of hub nodes in epileptogenic networks (14).…”

Section: Introductionsupporting

confidence: 92%

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Understanding brain network modifications operating at different time scales in the interictal state and during seizures is a very active stream of research. This is in line with computational neuroscience studies modeling neurological diseases as brain network disease (6)(7)(8)(9)(10)(11).…”

Section: Introductionsupporting

confidence: 81%

Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures

et al. 2020

View full text Add to dashboard Cite

Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics.

show abstract

“…A paradigmatic example of these issues is the estimate of brain functional connectivity from recordings of magneto/electro-encephalographic (M/EEG) data, currently a hot topic in neuroscience. Functional connectivity is systematically used to study both the healthy [10] and the pathological [38,41] brain, either at rest [4] or during the execution of specific tasks [25,43].…”

Section: Introductionmentioning

confidence: 99%

On the two-step estimation of the cross-power spectrum for dynamical linear inverse problems

et al. 2020

View full text Add to dashboard Cite

We consider the problem of reconstructing the cross-power spectrum of an unobservable multivariate stochatic process from indirect measurements of a second multivariate stochastic process, related to the first one through a linear operator. In the two-step approach, one would first compute a regularized reconstruction of the unobservable signal, and then compute an estimate of its cross-power spectrum from the regularized solution. We investigate whether the optimal regularization parameter for reconstruction of the signal also gives the best estimate of the cross-power spectrum. We show that the answer depends on the regularization method, and specifically we prove that, under a white Gaussian assumption: (i) when regularizing with truncated SVD the optimal parameter is the same; (ii) when regularizing with the Tikhonov method, the optimal parameter for the cross-power spectrum is lower than half the optimal parameter for the signal. We also provide evidence that a one-step approach would likely have better mathematical properties of the two-step approach. Our results apply particularly to the brain connectivity estimation from magneto/electro-encephalographic recordings and provide a formal interpretation of recent empirical results.

show abstract

Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity

Cited by 57 publications

References 101 publications

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures

On the two-step estimation of the cross-power spectrum for dynamical linear inverse problems

Contact Info

Product

Resources

About