Elsevier

Epilepsy & Behavior

Volume 22, Supplement 1, December 2011, Pages S110-S118
Epilepsy & Behavior

Electrical probing of cortical excitability in patients with epilepsy

https://doi.org/10.1016/j.yebeh.2011.09.005Get rights and content

Abstract

Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the ‘state’ of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep–wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Highlights

► A new method for monitoring the brain using electrical stimulation is introduced. ► Changes in neural activity associated with sleep and epilepsy are tracked. ► The method has the potential to be used for epileptic seizure prediction.

Introduction

The ability to predict or anticipate epileptic seizures has the potential to revolutionize the treatment of epilepsy by enabling the application of preventative therapy. Furthermore, successful anticipation1 of seizures will provide dramatic increases in the quality of life by potentially providing a warning system for the millions of people worldwide with uncontrolled seizures.

Over recent decades, the majority of seizure prediction algorithms that have been proposed in the literature have been developed using passively recorded intracranial electroencephalography (iEEG). Although most methods are mathematically quite varied, the majority are conceptually similar and focus on measuring the degree of order or complexity observed in the electrical fields of the brain. A decrease in complexity or disorder is thought to be indicative of abnormal hyper-synchronous dynamics associated with a pre-seizure time period. Earlier algorithms involved estimating entropy, correlation dimension, and short-term Lyapunov exponents [1], [2], [3], [4], [5].

The research focus has shifted to include synchronization analysis after the aforementioned methods failed to deliver repeatable results among different groups [6], [7], [8], [9]. These synchrony-based algorithms have shown promise in certain patient groups, but they have not delivered sufficiently reproducible outcomes and, therefore, have not provided satisfactory clinical performance [10], [11].

A major limiting factor in the development and validation of seizure prediction algorithms has been the lack of long-term iEEG data. Typically, iEEG data is captured from epilepsy patients undergoing planning for resective surgery. The monitoring period is normally limited to 1–2 weeks due to risks of infection and other complications. Therefore, it is not uncommon for only a small number of seizures, which may be atypical due to drug withdrawal and surgical recovery, to be recorded. The small number of atypical seizures combined with the heterogeneity of epileptic pathologies makes seizure prediction algorithm development difficult.

Another difficulty faced by seizure prediction developers is the interpretation of the spontaneously recorded iEEG signal. Although the iEEG is perhaps the best available clinical tool for recording the spatiotemporal dynamics of the human brain, the problem of uncovering the generators of the electrical fields is still considered ill-posed. For example, most experimental paradigms in cognitive neuroscience require averaging EEG responses to many (hundreds of) stimuli to reveal networks involved in specific brain function. This is because the subtle dynamics in the responses are hidden in background fluctuations of ongoing brain activity.

In this paper, a framework is presented for obtaining electrical-evoked potentials (EEPs) from the epileptic foci. The EEPs enable averaging out of the spontaneous fluctuations of ongoing brain processes. After averaging, the iEEG provides an indication of the brain's ability to respond to stimulation, which is a reflection of cortical excitability. By periodically tracking EEPs, we can monitor cortical excitability and potentially develop more robust seizure anticipation methods.

Probing the brain refers to applying a stimulus to the brain to measure the transient or steady-state response in order to quantify excitability, where a large response indicates high excitability. In this paper, we use the term excitability in a non-specific manner, where, for example, high excitability may result from low levels of inhibition. It is hypothesized that changes in excitability are a necessary condition for epileptic seizures. Therefore, tracking excitability may lead to a clinically effective seizure prediction method. The notion of using a probing stimulus to detect critical changes in complex dynamical systems has been applied extensively to other systems [12], but represents a significant paradigm shift in the study of brain dynamics and the field of seizure prediction.

Since the pioneering work of Penfield [13], electrical stimulation of the brain is the gold standard for cortical mapping in surgical planning for epilepsy patients. The cortical mapping allows clinicians to map the spatial aspects of both functional and pathologic tissue by observing behavioral responses to stimuli. More recently, iEEG responses to single pulse electrical stimulation (SPES) have been used to better identify reverberant cortical circuits associated with epileptic pathologies [14], [15], [16], [17]. The success in using an input stimulus in mapping the spatial aspects of the epileptic pathologies motivates using an input to map the temporal transitions from normal dynamics to seizures.

The first reports (to the authors' best knowledge) of probing the brain for epileptic seizure anticipation used magnetoencephalographic (MEG) responses to intermittent photonic stimulation to measure cortical excitability [18]. This methodology was specifically targeted to patients with photosensitive epilepsy. Using this approach, the authors developed a measure named the relative phase clustering index (rPCI) to quantify changes in excitability. With this measure, they showed that phase clustering in a harmonic of the stimulus frequency (gamma band) increased as a function of the time to next seizure.

The PCI measures the phase dispersion in the iEEG response to electrical stimulation, assuming a stationary sinusoidal steady-state response. The rPCI gives a measure of phase entrainment of the iEEG in the harmonics of the stimulation frequency, capturing nonlinear phenomena. This feature is only valid for steady-state responses with a stationary frequency, making application to single-pulse stimulation impractical.

The rPCI methodology was extended to a more general scenario using a sub-threshold electrical stimulus as a probe [19]. This study demonstrated that the time to the next seizure could be reliably predicted from the rPCI measure in a small group of MTLE patients. Furthermore, this study demonstrated how this measure could be used to localize epileptic networks. Due to the limited number of patients, Kalitzin et al. acknowledged that the results only provide a proof of principle for this approach and further validation of the approach is required.

Another study used iEEG responses to electrical stimulation to determine cortical excitability in order to monitor neural plasticity associated with a seizure onset zone in epilepsy surgery patients [20]. Although this measure was not used to track temporal changes associated with transitions from normal to a preseizure state, the study further demonstrates the potential for using the brain's responses to electrical stimulation to monitor cortical excitability. Further to this, a recent review discussing the use of direct electrical stimulation of the brain as an under-utilized tool for epilepsy research noted the need for more clinical investigations into the use of electrical stimulation for seizure prediction [21].

Recent theoretical evidence suggests an active approach is required for seizure anticipation [22]; it was shown that changes in excitability in a computational model of TLE could only be tracked when an active paradigm was employed. Further theoretical evidence of the requirement of an active approach for seizure prediction is provided by O'Sullivan-Greene et al. [23], who showed that the failure of EEG-based seizure prediction algorithms is not necessarily because the correct EEG feature has not been found to create the typical machine learning expert system, but rather that seizure precursors are not observable from the passively recorded EEG signal itself. That is, the passive EEG only allows the observation of a very small fraction of the underlying generators of brain activity. This indicates that seizure anticipation from passive EEG is unlikely to succeed. However, it was shown (through computer simulation) that the clever use of a probing stimulus can extract information from the EEG to facilitate seizure anticipation, even if the underlying neurodynamics cannot be observed in the passive EEG measurements [24]. Further theoretical justification for the use of probing stimulation was provided by Kalitzin et al. [25], who showed that a small perturbation of the cortical dynamics may enable the measurement of the likelihood of seizure occurrence.

Electrical stimulation is not the only method suitable for probing the brain to track excitability. For example, it has been demonstrated that cortical excitability in patients with idiopathic generalized epilepsy or focal epilepsy (including TLE) can be measured by probing the brain using transcranial magnetic stimulation (TMS) [26]. Stimulations were delivered to the motor cortex and excitability was evaluated using electromyography (EMG). It was found that patients whose seizures were resistant to drug therapies had hyper-excitable networks. Furthermore, excitability was elevated in a preseizure period. In similar research, using an acoustic auditory stimulus, Lin et al. found significant changes in the timing of event-related synchrony around the time periods where seizures occurred [27]. It was found that epileptic tissue synchronized faster than that of normal tissue, implying preseizure hyper-excitability. Other modalities for tracking preseizure hyper-excitability include near infrared spectroscopy (NIRS) [28] and functional magnetic resonance imaging (fMRI) [29]. The major benefit in using electrical stimulation with iEEG over other modalities is the ability to incorporate the stimulation and measurement system into an implantable seizure advisory/control device. A benefit in using electrical stimulation over a sensory-evoked potential is the ability to excite tissue without generating a percept that may be poorly tolerated over long periods of time.

Section snippets

Patients

The iEEG data used in this study were collected from two patients undergoing evaluation for the surgical resection of epileptic foci at St. Vincent's Hospital in Melbourne, Australia. Data were collected under ethics approval from St. Vincent's Human Research Ethics Committee (HREC-A 006/08). The standard clinical practice for epilepsy related surgery involves a diagnostic period of 1 week, where iEEG electrodes are implanted subdurally, directly on the surface of the brain. Images of the

Results from EEP amplitude analyses

The temporal evolution of the EEP amplitudes is shown in Fig. 4 for both patients. Parts (a) and (b) of the figure show the EEP amplitudes for all available channels in the differential montage for Patients 1 and 2, respectively. The red stars to the left of the figures indicate the position of the focal channels in the montage (as shown in Fig. 2). Parts (c) and (d) show a selection of channels, comparing the extracted features to epileptic events and a sleep–wake period for Patient 1.

Figs. 4

Discussion

This paper introduces a new method for tracking cortical excitability by using an electrical probing stimulus. This work builds on other studies that use an active approach for measuring excitability, providing evidence that the use of an input stimulus may improve seizure anticipation algorithms.

The current study extends the work of Kalitzin et al. [19]; these authors used steady-state EEP responses from the mesial temporal lobe using periodic stimulation, whereas we use repetitive single

Conflict of interest statement

None of the authors have a conflict of interest.

Acknowledgments

The authors would like to thank the patients who participated in this study. The Bionics Institute acknowledges the support it receives from the Victorian State Government through the Operational Infrastructure Support Program. The authors would also like to acknowledge the L.E.W. Carty Charitable Fund for supporting this research.

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