How Would You Like Your Epileptic Network? Linear, Nonlinear, Virtual?

Commentary

The study of electroencephalographic (EEG) synchronization during epileptic seizures recorded with intracerebral EEG dates back to the very early days of the application of computer analysis to the EEG, with the pioneering work of Mary Brazier.[1] Brazier’s coherence-based method allowed propagation patterns to be traced between intracerebral sites; it was subsequently improved,[2] but a major step was made when nonlinear methods of association were introduced.[3] After falling out of favor for 25 years, these methods are now reappearing in the ubiquitous context of the study of epileptic networks, with more powerful analysis approaches. We discuss here 2 articles measuring interactions in intracerebral EEGs, constructing networks, and attempting to draw conclusions from network properties regarding epileptogenicity.

The study of Müller et al[4] evaluates the linear relationships measured by coherence and the nonlinear relationships measured by mutual information during a 3-minute preictal period in 20 patients with bilateral temporal electrode implantations. Nonlinear measures usually reflect both linear and nonlinear relationships but in this study the nonlinear relationships represent the excess part of the relationship which cannot be explained by linear relationships.

The study finds that linear relationships are equally present across patients in the more abnormal, and subsequently resected, mesial structures, and in the less abnormal contralateral structures. In contrast, the excess nonlinear relationships were less uniformly distributed across patients and were predominant on the more abnormal side and also in interhemispheric interactions. The study also found that, in patients with successful surgery, the nonlinear relationships were mostly present in regions subsequently resected. The authors interpreted these findings as indicating that linear relationships may represent physiological interactions driven by mesial temporal anatomy whereas nonlinear relationships could reflect pathological activity.

Indeed, one could envisage as an explanation for these findings that the frequent interictal spikes present in epileptic mesial temporal structures lead to nonlinear interactions whereas the physiological background EEG may interact through linear relationships. It would have been interesting to relate the findings from this network analysis to the more traditional markers of epileptic regions, interictal spikes.

In the paper of Kini et al,[5] the epileptic network of 28 patients with subdural electrodes is investigated in an effort to determine whether its properties can have predictive value for surgical outcome. The epileptic network is defined by a linear method only, defining a network link by the value of coherence between 2 electrodes during preictal and ictal periods. A high coherence defines a high level of synchronization between 2 electrodes. The synchronizability of the whole network (the set of interactions between all the electrodes) can be viewed as how easily the network can support oscillatory dynamics given the connection strengths that are calculated using coherence.

One can then calculate the change in synchronizability of the network when removing the contacts of one region. If the network synchronizability increases after removing the contacts of one region, that region has a desynchronizing effect on the network. Conversely, if the network synchronizability decreases after the removal, the region has a synchronizing effect on the network. Removing a set of contacts and recalculating the network characteristics can be termed a “virtual resection” since it allows assessing how a network reacts when a part of it is removed. The concept of virtual resection is currently applied by a French multicenter study in a clinical trial using a very complex whole brain model, which integrates anatomical and tractography information as well as seizure propagation information from intracerebral electrodes.[6]

Kini et al found that synchronizability decreased from preseizure to seizure and decreased significantly more during the early part of the seizure in patients who became seizure-free. Remarkably, this difference allowed discriminating patients with good outcome from patients with poor outcome with an accuracy of 86%. This difference represents a characteristic of the global network; it does not provide guidance regarding which regions should be resected. When the authors compute the change in synchronizability of the epileptic network resulting from virtually resecting the subsequently resected region (trying to assess if the subsequently resected region had a synchronizing or desynchronizing effect on the epileptic network), they did not find statistically significant differences that predicted outcome. They found interesting differences in specific frequency bands (β in one patient subgroup and γ in another subgroup) but these were not significant after the necessary correction for multiple comparisons; these changes are encouraging however, and may point to a potential use of the virtual resection method after validation in a larger patient group. One could speculate that the inclusion of nonlinear measures of interaction, not used in this study, could increase the statistical power of the analysis, particularly if, as speculated by Müller et al, such nonlinear relationships reflect the interactions of epileptic activity.

The study of epileptic networks recorded with intracranial electrodes, with linear or nonlinear methods, is not a new concept but new approaches may allow a better understanding of which are the regions critical for seizure generation and maintenance. Whereas early studies analyzed seizures only, recent studies analyze seizures as well as interictal epochs and both provide valuable information. One must remember, however, that no study will free us from the fundamental weakness of intracranial recordings, that of spatial undersampling. Maybe more effort could be made toward determining the characteristics of the network specific to the situation when we are not recording from the epileptogenic zone: the properties of the epileptic network in patients who fail surgery may not reflect an intrinsic property of the seizures of such patients, but the fact that we have an incomplete and poorly representative image of the seizure.