Graph analysis of epileptogenic networks in human partial epilepsy

Abstract

SUMMARY Purpose The current gold standard for the localization of the cortical regions responsible for the initiation and propagation of the ictal activity is through the use of invasive electrocorticography (ECoG). This method is utilized to guide surgical intervention in cases of medically intractable epilepsy by identifying the location and extent of the epileptogenic focus. Recent studies have proposed mechanisms in which the activity of epileptogenic cortical networks, rather than discrete focal sources, contributes to the generation of the ictal state. If true, selective modulation of key network components could be employed for the prevention and termination of the ictal state. Methods Here, we have applied graph theory methods as a means to identify critical network nodes in cortical networks during both ictal and interictal states. ECoG recordings were obtained from a cohort of 25 patients undergoing presurgical monitoring for the treatment of intractable epilepsy at the Mayo Clinic (Rochester, MN, U.S.A.). Key Findings One graph measure, the betweenness centrality, was found to correlate with the location of the resected cortical regions in patients who were seizure-free following surgical intervention. Furthermore, these network interactions were also observed during random nonictal periods as well as during interictal spike activity. These network characteristics were found to be frequency dependent, with high frequency gamma band activity most closely correlated with improved postsurgical outcome as has been reported in previous literature. Significance These findings could lead to improved understanding of epileptogenesis. In addition, this theoretically allows for more targeted therapeutic interventions through the selected modulation or disruption of these epileptogenic networks.

The trees and the forest: Characterization of complex brain networks with minimum spanning trees

“…Thus network analysis shows that the epileptic network may extend beyond the epileptic focus, and this could have implications for epilepsy surgery: removal of a critical node, even if it is not in or near the focus, could abort the seizures. A similar result, obtained with more conventional network analysis, has been reported more recently by another group (Wilke et al, 2011).…”

“…While most studies agree that functional brain networks become more regular (higher clustering and longer path length) during seizures, in the interictal state both increased randomness (reflected by a lower clustering coefficient and shorter path length) as well as increased regularity have been reported (Kramer and Cash, 2012;van Diessen et al, 2013). With respect to the significance of hubs in epilepsy there is more agreement: several studies suggest that pathological hubs are more prevalent in epilepsy and that the removal of these hub nodes is associated with a more favourable outcome of epilepsy surgery (Ortega et al, 2008;van Diessen et al, 2013;Wilke et al, 2011). The application of modern network theory to brain neuroscience has thus improved our understanding of the development and organization of brain networks and their relation to cognition.…”

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

“…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.…”

“…In other words, betweenness centrality is a measure of the “importance” of a node to transfer information across the network. Unlike other measures that quantify network properties for a node, the betweenness centrality depends not only on the primary efferent and afferent connections to a node, but also on the secondary and tertiary connections (Wilke, Worrell, & He, 2011). …”

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

“…It is expected that targeting only the hubs of functional networks would make therapies more effective and potentially even less invasive. In the past, there have been a number of studies aiming at 1) the definition of different types of functional networks -based on various EEG-derived signal interdependencies used to define the networks links (edges), and 2) the investigation of how distinct topological properties of these networks change before, during and after epileptic seizures (Ponten et al, 2007;Schindler et al, 2008;Kramer et al, 2010;Wilke et al, 2011;van Mierlo et al, 2011;Varotto et al, 2012). For example, in (Schindler et al, 2008;Kramer et al, 2010) it was found that initial ictal network dynamics are characterized by strong modularization, i.e.…”