How important is the seizure onset zone for seizure dynamics?

Geier, Christian; Bialonski, Stephan; Elger, Christian E.; Lehnertz, Klaus

doi:10.1016/j.seizure.2014.10.013

Cited by 58 publications

(59 citation statements)

References 57 publications

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“…Other studies only reported an increase in the betweenness centrality in the gamma band (Varotto, Tassi, Franceschetti, Spreafico, & Panzica, 2012; Wilke et al, 2011), or in a few seconds prior to seizure onset (Li et al, 2016). Geier, Bialonski, Elger, and Lehnertz (2015) found that the betweenness centrality in pre‐ictal ECoG (using cross‐correlation) was highest in brain regions neighboring the SOZ. This idea is supported by results for one of our patient, see the example in (Figure 7).…”

Section: Discussionmentioning

confidence: 99%

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Blooijs

Leijten

Rijen

et al. 2018

Human Brain Mapping

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We investigated effective networks constructed from single pulse electrical stimulation (SPES) in epilepsy patients who underwent intracranial electrocorticography. Using graph analysis, we compared network characteristics of tissue within and outside the epileptogenic area. In 21 patients with subdural electrode grids (1 cm interelectrode distance), we constructed a binary, directional network derived from SPES early responses (<100 ms). We calculated in‐degree, out‐degree, betweenness centrality, the percentage of bidirectional, receiving and activating connections, and the percentage of connections toward the (non‐)epileptogenic tissue for each node in the network. We analyzed whether these network measures were significantly different in seizure onset zone (SOZ)‐electrodes compared to non‐SOZ electrodes, in resected area (RA)‐electrodes compared to non‐RA electrodes, and in seizure free compared to not seizure‐free patients. Electrodes in the SOZ/RA showed significantly higher values for in‐degree and out‐degree, both at group level, and at patient level, and more so in seizure‐free patients. These differences were not observed for betweenness centrality. There were also more bidirectional and fewer receiving connections in the SOZ/RA in seizure‐free patients. It appears that the SOZ/RA is densely connected with itself, with only little input arriving from non‐SOZ/non‐RA electrodes. These results suggest that meso‐scale effective network measures are different in epileptogenic compared to normal brain tissue. Local connections within the SOZ/RA are increased and the SOZ/RA is relatively isolated from the surrounding cortex. This offers the prospect of enhanced prediction of epilepsy‐prone brain areas using SPES.

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Section: Discussionmentioning

confidence: 99%

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Blooijs

Leijten

Rijen

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…On the contrary, the resected area of Patients 6-7 had no overlap with the marked SOZ, which posed the question: How reliable is the visually marked SOZ in such cases? Although the relationship between the SOZ resection and the post-surgical outcome is challenged by recent studies (Rummel et al, 2015;Geier et al, 2015;Huang et al, 2012), it is generally agreed that resection of critical parts of the SOZ is a sufficient condition to attain long-term seizure freedom (Rosenow and Lüders, 2001). Under this premise, we first assumed that the true SOZ of these patients had not been resected.…”

Section: Seizure Onset Identification and Retrospective Interpretationmentioning

confidence: 99%

Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification

Vila-Vidal

Príncipe

Ley

et al. 2017

Clinical Neurophysiology

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“…A mechanistic understanding of seizure generation and evolution may be derived from spatial and temporal dynamics of the epileptic network (Wendling et al, 2003; Jerger et al, 2005; Schindler et al, 2007; Schevon et al, 2007; Schindler et al, 2008; Zaveri et al, 2009; Kramer et al, 2010; Jiruska et al, 2013; Rummel et al, 2013; Weiss et al, 2013; Burns et al, 2014; Geier et al, 2015; Khambhati et al, 2015, 2016). In this framework, network nodes are intracranial sensors measuring the electrocorticogram (ECoG) and network connections are time-varying statistical relationships between sensors (Friston, 2011; Hutchison et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The degree of connectivity between brain regions is related to the synchronization of neural populations, a putative generator of dysfunction in epilepsy. Brain regions that are topologically central to the epileptic network tend to lie within (Wendling et al, 2003; Jerger et al, 2005; Schindler et al, 2007, 2008; Kramer et al, 2010; Jiruska et al, 2013; Burns et al, 2014; Khambhati et al, 2015) and adjacent to (Schevon et al, 2007; Zaveri et al, 2009; Rummel et al, 2013; Weiss et al, 2013; Geier et al, 2015) clinically defined seizure-onset zones (SOZs) during interictal, preictal, and ictal epochs (Zaveri et al, 2009; Warren et al, 2010; Khambhati et al, 2015). In this context, it is interesting to ask the question: if network dysfunction persists over long time scales, then (1) how does network topology drive brain dynamics differently during interictal and ictal epochs, and (2) how might aberrant brain regions disrupt functional interactions underlying normal function?…”

Section: Introductionmentioning

confidence: 99%

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy

Khambhati

Bassett

Oommen

et al. 2017

eNeuro

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Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.

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How important is the seizure onset zone for seizure dynamics?

Cited by 58 publications

References 57 publications

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy

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