Detecting seizure origin using basic, multiscale population dynamic measures: Preliminary findings

https://doi.org/10.1016/j.yebeh.2008.09.008Get rights and content

Abstract

Many types of electrographic seizures are readily identifiable by direct visual examination of electroencephalographic or electrocorticographic recordings. This process can, however, be painstakingly slow, and much effort has been expended to automate the process using various dynamic properties of epileptiform waveforms. As methods have become more subtle and powerful they have been used for seizure subclassification, seizure prediction, and seizure onset identification and localization. Here we concentrate on the last, with reference to seizures of neocortical origin. We briefly review some of the methods used and introduce preliminary results from a very simple dynamic model based on key electrophysiological properties found in some seizure types: occurrence of very fast oscillations (sometimes called ripples), excess gamma frequency oscillations, electroencephalographic/electrocorticographic flattening, and changes in global synchrony. We show how this multiscale analysis may reveal features unique to seizure onset and speculate on the underlying cellular and network phenomena responsible.

Introduction

Detecting electrographic changes associated with seizures in patients with drug-intractable epilepsy is a critical component of the “workup” to surgery. Painstaking, epoch-by-epoch analysis of a patient’s electroencephalogram (EEG), followed by more detailed analysis of periseizure recordings may guide surgical intervention toward specific problem areas in the absence of clear imaging and semiological clues. Conventional dipole models of surface EEGs, however, may give “ghost” sources, and rarely localize seizures with sufficient spatial resolution to accurately guide surgical resection. More detailed analysis of the dynamics of seizure events has been used, or at least proposed as useful, for more than 40 years [1]. Simple linear analysis has been used extensively and shown to capture some of the features of seizure foci quite robustly. For example cross-correlations may determine the location of some epileptogenic foci [2], and changes in the relative scale of higher compared with lower EEG frequencies can be a reliable indicator of cortical neuronal state [3] and have been shown to signal the start of seizures in many cases [4]. Also, rule-based methods have proved very robust in separating epileptiform events from background [5]. Wavelet-based analyses, examining frequency changes on multiple scales, have taken this approach further, successfully detecting seizures with reduced false positives [6]. Principal component analyses, and other methods dependent on correlation matrices, have also been used to compare across multiple concurrent channels of data and have been shown to be useful in detection of abnormal EEG signatures of epilepsy [7], [8]. More complex and mathematically elegant methods often use nonlinear time series analysis to more accurately reflect the nature of electrographic data. Nonlinear patterns of synchrony may often reveal very different degrees of intercortical temporal interaction associated with seizures when compared with linear methods [9].

Any method for localizing epileptic foci must include a feature for detecting the precise time at which transition from normal to ictaform activity occurs. Once this is achieved, it is then possible to identify the location (electrode number) of the initial transition and plot its spread to neighboring cortical areas (electrodes). This is a nontrivial problem, particularly evidenced by the power of nonlinear analytical techniques to quantify very subtle changes in cortical dynamics. The main issue is one of time scale: Simple linear methods such as power spectral density show abrupt (on the order of 0.1 second) changes in dynamics [10] at onset of electrographic seizures. However, many dynamic analyses can detect changes several tens of seconds before the electrographic event [11]. In addition, variations on nonlinear, spatiotemporal correlations have shown clear changes in cortical dynamics some tens of minutes before seizures [12], [13], [14]. These long-term changes may flag alterations in the plastic state of neuronal networks or—not mutually exclusively—perhaps slow changes in homeostatic state of discrete cortical regions. Riding on top of these changes are presumed to be alterations in more rapid response states in networks that predispose highly localized cortical regions to fulminant changes in dynamic state (see below). It is the signature of these short time scale events that this article considers in more detail.

Within the above framework we must ask which dynamic features of electrographic data can signal these short time scale changes. The onset of electrographic seizures often manifests as high-amplitude EEG signals with abrupt appearance of prominent high-frequency events (80 Hz to several hundreds of hertz, very fast oscillations (VFOs), or ripples) that are rarely seen in normal intracranial EEG records, suggesting a specific relationship to electrographic seizures [15]. In focal epilepsies, VFOs are prevalent immediately prior to seizure generation and are spatially constrained to the cortical seizure focus [4], [16], [17], [18], [19], [20], [21]. In addition, gamma frequency oscillations (30–80 Hz) have been reported to occur suddenly at high amplitude in many temporal lobe epilepsies [22], [23], [24]. As with VFOs, gamma rhythms appear to signal local changes, in terms of both their power [23] and their pattern of phase synchronization [25]. Although there is some semantic confusion over the nature of VFOs and gamma rhythms—VFO sometimes being referred to also as a type of gamma rhythm [26]—it is clear from in vitro studies that these two EEG bands have different underlying cellular and network mechanisms [27], with gamma rhythms critically dependent on synaptic inhibition and VFOs (variously “high gamma,” ripples, high-frequency oscillations) being generated by gap junctional connections in networks of neurons [28]. They will therefore be treated separately in the present study.

In addition to the sudden increase in higher-frequency EEG bands (gamma and VFO, above), many focal electrographic seizures are accompanied by a decrease in low-frequency EEG bands, termed the electrodecremental response [4]. However, the interrelationship between low- and high-frequency bands is variable between patients with temporal lobe epilepsy. An initial slow wave at the start of the electrographic seizure may or may not be present with the faster rhythms, and different apparent origins of these initial slow waves, when present, are associated with different modal frequencies of accompanying faster rhythms [29], [30]. In each case a different underlying mechanism of seizure generation is proposed.

Finally, for a focal seizure to manifest clinically, activity must spread from the point (or points) of origin to generate an experiential, motor, or behavioral correlate. We therefore wished to combine measures of the aforementioned four features (VFO, gamma, decreased slow rhythms, and synchrony) of some focal electrographic seizures to see whether these factors alone were sufficient to signal a rapid change in cortical dynamics unique to seizure onset. From this we attempted to see whether such a combined measure could be of possible use in focus localization.

Section snippets

Methods

Subdural recordings were made from a 32- or 48-contact platinum array (Ad-Tech) placed over the region of neocortex identified as the putative focus from scalp EEG measures and seizure semiology in two patients. Recordings were made using either Neuroscan or Xltek equipment, with output bandwidth up to 1 kHz (patient A) or 500 Hz (patient B) with a reference on a distant contact on the subdural strip; the EEG was then re-referenced to a common average signal. Patient A was a 5-year-old girl with

Results

Raw electrocorticographic recordings show bursts of VFO and gamma rhythms on many channels, often simultaneously. Long (several seconds) runs of these high-frequency rhythms occur around the subjective estimate of electrographic seizure onset (Fig. 1). However, any quantitative measure or seizure onset must distinguish between the high-frequency activity associated with interictal-like bursts and physiological sharp waves, the occurrence of which may or may not be related to the underlying

Discussion

The present brief examples demonstrate that basic dynamic changes in focal epilepsies of neocortical origin may be useful in localizing the origin of observed electrographic seizures. Quantification of the electrodecremental response, coupled with a measure of how cortical areas communicate with each other through synchrony [34], allows transient changes in dynamics to be seen that correspond to a seizure focus or foci. The changes seen were of sufficient brevity and magnitude to allow spatial

Conflict of interest statement

The authors here declare that the development of this study does not imply any kind of conflict of interest.

Acknowledgments

We thank the Wolfson Foundation and the MRC (Milstein award) for financial assistance.

References (45)

  • G.F. Ayala et al.

    The genesis of epileptic interictal spikes

    Brain Res

    (1973)
  • N. Wiener

    Cybernetics or control and communication in the animal and the machine

    (1968)
  • N.J. Mars et al.

    Propagation of seizure activity in kindled dogs

    Electroencephalogr Clin Neurophysiol

    (1983)
  • M. Mukovski et al.

    Detection of active and silent states in neocortical neurons from the field potential signal during slow wave sleep

    Cereb Cortex

    (2007)
  • R.S. Fisher et al.

    High-frequency EEG activity at the start of seizures

    J Clin Neurophysiol

    (1992)
  • S. Ghosh-Dastidar et al.

    Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection

    IEEE Trans Biomed Eng

    (2008)
  • A. Ochi et al.

    Dynamic changes of ictal high-frequency oscillations in neocortical epilepsy: using multiple band frequency analysis

    Epilepsia

    (2007)
  • K.K. Jerger et al.

    Comparison of methods for seizure detection

  • C.E. Elger et al.

    Seizure prediction by non-linear times series analysis of brain electrical activity

    Eur J Neurosci

    (1998)
  • J. Martinerie et al.

    Epileptic seizures can be anticipated by non-linear analysis

    Nat Med

    (1998)
  • G.A. Worrell et al.

    High-frequency oscillations and seizure generation in neocortical epilepsy

    Brain

    (2004)
  • A. Bragin et al.

    High-frequency oscillations in human brain

    Hippocampus

    (1999)
  • Cited by (15)

    • Integration of stationary wavelet transform on a dynamic partial reconfiguration for recognition of pre-ictal gamma oscillations

      2018, Heliyon
      Citation Excerpt :

      Urrestarazu and colleagues who confirmed that fast oscillations are the best hallmark of epileptogenic region. In order, to detect and characterize seizure onset, Ropun and his colleagues used a multi scale analysis of gamma-band oscillations [9]. Another separation method was proposed by Xiaoli based on empirical mode decomposition.

    • Ictal high-gamma oscillation (60-99Hz) in intracranial electroencephalography and postoperative seizure outcome in neocortical epilepsy

      2012, Clinical Neurophysiology
      Citation Excerpt :

      Proper resections of ictal fast activities or HFOs were recently suggested to be correlated with good seizure outcomes (Modur et al., 2011; Nariai et al., 2011). On the other hand, ictal and preictal synchrony change may be complex and both synchronisation and desynchronisation were suggested to occur (Wendling et al., 2003; Bartolomei et al., 2004; Roopun et al., 2009; Jiruska et al., 2010a). In a few studies, ictal hypersynchronous clusters were suggested to be related to the epileptogenic zone and ictal onset (Roopun et al., 2009; Jiruska et al., 2010a).

    • Dynamical study of metallic clusters using the statistical method of time series clustering

      2011, Computer Physics Communications
      Citation Excerpt :

      In our method, we draw attention to novel ways of analyzing the correlation matrix and demonstrate, by illustration of several small clusters using CNA, how the effective variables (to be defined below) are obtained and applied to describe the dynamics of our physical system. We should emphasize that the time series clustering method was developed for larger complex systems in mind, and has in fact been successfully applied to finance [20,21], neuroscience [22–26], and meteorology [27–29]. We have, for instance, very recently applied the method to financial markets (500–3000 degrees of freedom) [30] and global positioning system networks (100 degrees of freedom) [31].

    View all citing articles on Scopus
    View full text