Seizure prediction using scalp electroencephalogram

Ivo Drury,a,b,* Brien Smith,b Dingzhou Li,c and Robert Savita,c,d

a Diagnostic Neurodynamics LLC, Ann Arbor, MI 48104, USAb Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA

c Department of Physics, University of Michigan, Ann Arbor, MI 48109, USAd Biophysics Research Division, University of Michigan, Ann Arbor, MI 48109, USA

Received 15 June 2003; accepted 15 June 2003

Abstract

Using a measure of nonlinear dynamical changes we term marginal predictability, we report evidence of robust changes in this parameteron scalp EEG in a cohort of patients with medically refractory mesiobasal temporal lobe epilepsy (MBTLE). In the baseline (interictal) statethere are distinct differences in this nonlinear measure between epileptic and neurologically normal subjects. At baseline, in patients withMBTLE there are differences in these measures between electrodes adjacent to the ictal onset zone and more remotely placed electrodes.The character of these differences evolves over a period of approximately 30 min before a seizure. We discuss and integrate our findingswith two emerging concepts in epileptology, first, the concept of a preictal or transition phase rather than an abrupt movement from interictalto ictal activity, and second, the notion of an epileptic neural network with changes in areas of brain remote from what has traditionally beenconsidered the ictal onset zone influencing “ictogenesis.”© 2003 Elsevier Inc. All rights reserved.

Keywords: Mesial temporal lobe epilepsy; Nonlinear dynamics; Seizure prediction; Marginal predictability

Introduction

Despite a significant number of new antiepileptic com-pounds that have become available worldwide in the past 15years, epilepsy refractory to best available medical therapycontinues to affect nearly 1 in 4 patients with a seizuredisorder. Current estimates are that some 500,000 personshave uncontrolled seizures in the United States today. Evenwhen these patients’ seizures are relatively infrequent thereis a major impact on their quality of life (Gilliam, 2002).There is also sizeable morbidity and mortality and directand indirect economic burdens (Begley et al., 2000). Theavailability of reliable methods of seizure prediction couldenhance the quality and safety of patients with epilepsy,facilitate implementation of short-term interventions toabort a seizure, and have the potential of reducing theeconomic burden of this disease. Efforts to predict when a

seizure was going to occur began with Viglione and Walsh(1975), but have gathered momentum over the past decadewith the ready availability of high-speed computer process-ing and the application of sophisticated mathematical tech-niques to biological processes.

Part of the rationale for attempting to predict seizure onsetrelies on the fact that at least some patients with seizuresexperience a premonitory phase lasting for several minutes tohours that is distinct from an aura. There are, in fact, severalstrands of evidence from both human and experimental reportsthat support the notion of a transition phase of at least severalminutes and potentially up to 1 h before the ictal event properbegins and against the concept of ictal activity happening as ifa light switch had just been turned on.

Initial attempts at anticipating seizures relied on standardlinear statistical methods (Gotman et al., 1985; Osorio,1998). Following the renaissance of nonlinear dynamics andthe realization that many natural processes embodied non-linearities in their dynamics, researchers in a variety offields began looking for evidence of nonlinearities and spe-cifically chaos in a wide range of data sets. The hope was

* Corresponding author. Diagnostic Neurodynamics LLC, 728 Onon-daga Street, Ann Arbor, MI 48104, USA. Fax: �1-734-747-9239

E-mail address: idrury@comcast.net (I. Drury).

R

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that observing chaos in biological systems could, first, helpelucidate the underlying dynamics of various biologicalprocesses and, second, lead to new methods of predictingpotential deleterious events such as seizures and therefore tonew therapeutic interventions. Early attempts met withmixed results (Theiler, 1995; Theiler and Rapp, 1996; Pa-lus, 1994; Babloyantz and Destexhe, 1986, Iasemidis et al.,1990; Destexhe and Babloyantz, 1991). But a growing un-derstanding in the biological community of the nature ofchaos and of the probable nature of nonlinearities in manybiological systems led researchers to a more sophisticatedview of the role of nonlinear dynamics in their systems ofinterest. In studies of EEG, there is now a generally ac-cepted understanding that low dimensional chaos per se isnot likely to be manifest in most EEG data sets (Theiler andRapp, 1996). This understanding has led to a considerablymore sophisticated view of nonlinearities in EEG and to thedevelopment of methods of detecting such effects. In par-ticular, work over the past 5 years or so by several groups(Andrezjak et al., 2001; Arnhold et al., 1999; Casdagli et al.,1996; Protopopescu, 2001; Le Van Quyen et al., 2001;Manuca et al., 1998; Savit et al., 2001; Jerger et al., 2001)has focused on a variety of nonlinear measures, mostlybased on correlation integrals (Grassberger and Procaccia,1983). Unlike previous work, these more recent investiga-tions do not claim to detect chaos in EEG. Rather they takean empirical approach seeking to correlate values of non-linear measures with disease states either in space or in time.

Most of the work in seizure prediction has concentratedon analysis of intracranial EEG (ICEEG). ICEEG has tra-ditionally been available from a subset of epilepsy patientswith mesiobasal temporal lobe epilepsy (MBTLE) (Henryand Ross, 1992). In cases in which patients are candidatesfor surgical resection of the seizure focus, and in which thelocation of the focus cannot be determined accuratelyenough using noninvasive methods, patients may undergoICEEG. Such recordings, unlike scalp EEG, do not sufferfrom muscle noise or attenuation of the signal by bone andtissue. They are therefore very attractive data sets to studyfor the presence of nonlinear effects. Over the long term,however, ICEEG has two important limitations. First, asnoninvasive methods of determining a seizure focus im-proved, the number of ICEEG studies decreased, limitingthe available database. Second, one important goal of non-linear studies in epilepsy is to develop ambulatory monitor-ing methods with a view to being able to predict seizures.Clearly, an ambulatory monitor that relies on recordingsfrom scalp electrodes is likely to be much more easilytolerated and maintained than one that relies on ICEEG.

Here we report our experience using a particular nonlin-ear dynamical measure called marginal predictability in agroup of patients with MBTLE, with data derived exclu-sively from scalp EEG. We were interested in studying ashomogenous a set of patients with refractory epilepsy aspossible in the belief that this would increase the signifi-cance of our results. We report distinct differences at base-

line between the baseline EEG of the epileptic subject andthat of normal subjects. Moreover, the nature of this differ-ence in the epileptic patient changes about 30 min before aclinical seizure, well in advance of any identifiable changeon scalp EEG using conventional visual analysis. We dis-cuss the possible biological significance of our findings andimplications for future work in this field.

Methods

Epileptic patient selection

Patients were evaluated by two of the authors (ID, BS) orone of two other epileptologists at Henry Ford Hospital inDetroit. Presurgical evaluation at Henry Ford Hospital fol-lows a standardized protocol (Valachovic, 1998) similar tosuch assessments at most major epilepsy centers.

In an effort to provide as homogenous and thereforecomparable a group of patients as possible, we have limitedour analysis to patients with MBTLE, the most commonpatient group considered suitable candidates for epilepsysurgery. The specific criteria for inclusion in our analysiswere as follows:

● seizures of unilateral mesiobasal temporal lobe origin,documented by history and interictal and ictal EEGrecordings;

● age between 18 and 60 years;● no mass lesion detected with magnetic resonance im-

aging;● intelligence quotient of 70 or more;● no evidence of a progressive neurological disorder,

active neurological disorder other than epilepsy, andno other significant medical disorder, severe depres-sion, or psychosis;

● no evidence of damage to the hippocampus contralat-eral to the seizure focus as determined by magneticresonance imaging;

● no history of drug or alcohol abuse;● no barbiturate or benzodiazepine use, with the excep-

tion of intravenous benzodiazepines used for acuteseizure control;

● no history of drug use other than antiepileptic drugsduring the 2 weeks prior to the recordings.

All EEG recordings were reviewed by one of the authors(ID), an experienced epileptologist. EEG recordings fromthe patients were visually inspected to identify epochs ofinterest for analysis. Epochs were divided into the followingsets: (a) interictal, meaning at least 1 h before and at least1 h after a seizure, (b) preictal, meaning within the hourpreceding a seizure and at least 1 h following a seizure, and(c) ictal. Epochs were separated by behavioral state in afashion described below.

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Normal subject selection

Waking and sleeping EEG from normal age- and sex-matched subjects were also analyzed. All of these subjectsunderwent a complete medical and neurological history andcomprehensive neurological examination by one author(ID). Normal subjects had no history of drug and alcoholabuse and had not used any medications in the 2 weeks priorto the study.

EEG methods

EEG recordings are recorded on a 128-channnel BMSI/Nicolet 5000 System. A standard bipolar scalp–sphenoidalmontage was used. Recording durations varied from 2 to 10days based on the need to obtain at least three of thepatient’s habitual seizures. Antiepileptic drug doses weretapered and periods of sleep deprivation used in an effort to

facilitate seizure occurrence in individual patients. The bandpass was 0.5 to 100 Hz. The digital data were then trans-ferred to a Unix workstation for conversion to ASCII textdata and further analysis.

Behavioral state scoring

One of us (ID) reviewed the complete scalp EEG (26channels) for between 24 and 56 h per patient. Based onEEG features, he categorized the patient’s behavioral statefor each 30-s interval of the epochs reviewed, placing thatinterval into one of the categories shown in Table 1.

From the set of forty 30-s intervals for a given epoch, asummary behavior score for that epoch was produced. If 32or more of the 30-s intervals (80%) of a given epoch werein the same behavior state, then that 20-min epoch wasdeemed primarily in that behavior state, e.g., AEO or D2. If60–79% of an epoch was spent in one state, then that epochwas considered predominantly that state and indicated withthe prefix P, thus PAEO or PD2. When less than 60% of anepoch was spent in just one state, then the epoch wasconsidered a blend of two or more states, thus AEO/D2.

Mathematical considerations

A detailed mathematical treatment of our approach isprovided by Li et al. (in press). The essence of our approachin nonmathematical terms is illustrated in Fig. 1.

To understand how our methods work, consider the ex-ample in Fig. 1. In this example we are considering thedecimated time series, consisting of every third point. In theleft column we show four different epochs from the EEG,

Table 1EEG features of behavioral states

Behavioral state designation Criteria

A-EO: awake with eyes open Attenuation of �–� increased EMGand other movement artifact

A-EC: awake with eyesclosed

Posterior � or �, rhythm

D1: early drowsiness Diffuse �–�, SEMSD2: late drowsiness Loss of �–�, POSTs, V-wavesS2: stage 2 NREM Spindles, K-complexes, �20% 2 Hz �S3–4: stages 3 or 4 NREM �20% 2 Hz � of �75 �VREM REMs, low voltage mixed frequency

fast, sawtooth waves

Fig. 1. Schematic to explain underlying principles to calculation of marginal predictability. See text for detailed explanation.

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each containing four points. All four of these epochs are thesame. If this decimated EEG were completely deterministic,and if the current value of the EEG depended only on thepreceding four values, then the next value following each ofthese epochs should be the same. In the second column wesee the values that actually occur following these four ep-ochs. Clearly they are not the same, and so we need moreinformation than that contained in just the four precedingvalues to predict the EEG. What happens if we add a fifthpoint preceding the value we want to predict? In the rightcolumn we show the four epochs, but now with the fifthpreceding point included. Note that the first and third rowsnow form consistent patterns. That is, given the five pre-ceding values shown in the first and third rows, we canmake a good prediction of the next value. We were not ableto do that with only four preceding values. Therefore, thereis significant additional information in the fifth lag of thistime series that can be used to predict the next point in theseries, even after we have used the information in theintervening four lags. �d is a measure of how much addi-tional information there is in the dth lag, given that we haveused the information in the intervening d-1 lags.

The original EEG is sampled at 200 Hz. In order tominimize the value of the mutual information (Tong, 1990),we use a time series consisting of every third point of theoriginal EEG. The �’s and Q’s discussed here are computedon that time series.

EEG samples were divided into 20-min-long epochs.Interictal epochs were 20 min long while a preictal periodconsisted of three consecutive epochs, thus 60 min. Sampleswere analyzed in 40-s windows at 200 Hz, thus providing8000 data points. In this paper we present the results ofanalysis of forty-four 60-min preictal and forty-four 20-min-long interictal epochs from 14 patients with MBTLEplus twenty-four 20-min-long epochs from 6 nonepilepticsubjects.

Three important measures to be presented in the resultssection below are defined here as follows:

● �d: measure of additional predictive information in the(d � 1)st lag of a time series given that we havealready used information in the intervening d lags. If�d is close to zero, then there is no additional predic-tive information on average. In these results d � 2;

● Q2: difference between the �2’s of one electrode prox-imate to the ictal onset site to one more remote elec-trode.

● SPR, sum of positive ranks derived from Wilcoxon’ssum of signed-rank test. The tested null hypothesis isthat median �2 of electrodes adjacent to seizure onsetis the same as more remote electrodes. If SPR is closeto the expected average under the null hypothesis, thenthe null hypothesis cannot be rejected. If SPR is toolow one must accept the alternative hypothesis that Q2

� 0.

Results

Examples of �2 as a function of time are shown in Figs.2 and 3. Fig. 2(left) is the time course of �2 for a temporalelectrode (F8) ipsilateral to the side of ictal onset during a20-min interictal period, while the right is �2 for the ipsi-lateral occipital (O2) electrode. Note that the �2 (F8) issignificantly higher than the �2 (O2). Fig. 3 shows �2 for thesame electrodes used in Fig. 2, but calculated during a 1-hpreictal period immediately preceding a seizure. Note that�2 (F8) is still greater than �2(O2) until about 15 min beforethe seizure onset. Then, �2 (F8) decreases to approximatelythe same level as �2(O2).

Q2 is the difference between the �2’s of different elec-trodes. In Fig. 4 Q2 is greater than zero for the interictal

Fig. 2. Interictal �2 for F8 (left) and �2 for O2 (right) from a patient with right temporal onset partial seizures (20-min epochs).

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(left) epoch (typically close to 0.05). For most of the earlyportion of the preictal (right) epoch Q2 is also greater thanzero but moves close to and stays near zero starting about 15min prior to the seizure. Although there are differences inthe profiles of the �2’s for different epochs, the featuresillustrated in Fig. 4, namely the fact that Q2 is smaller in thepreictal compared to the interictal period, can be found inalmost all of the epochs studied in epileptic subjects.

The same analysis was also applied to a set of epochstaken from nonepileptic subjects. Fig. 5 is an example of a20-min epoch from a nonepileptic subject. The electrodes ofinterest here are ipsilateral temporal (left) and occipital(middle) electrodes. As can be seen from (right), of Fig. 5,the value of Q2 for nonepileptic subjects is typically close tozero, which suggests that there is no systematic difference

between the �2 of temporal and occipital electrodes in non-epileptic subjects.

For statistical validation, we performed Wilcoxon’ssummed-rank test to test the null hypothesis that the medianof �2 of the electrode adjacent to seizure onset, �adj (F7 orF8, depending on which hemisphere the seizure originatedfrom), is the same as that of the electrode remote to the sideof seizure onset �remote (in this case the occipital electrodeO1 or O2 ipsilateral to the site of ictal onset), which meansthat Q2 � 0, statistically. The sum of positive ranks as afunction of time is shown in Figs. 6, 7, and 8 for interictal,preictal, and nonepileptic subjects, respectively. If SPR isclose to the average under the null hypothesis (the brokenline in the middle of the graph), then we cannot reject thenull hypothesis. However, if SPR is too low, we must reject

Fig. 3. Preictal �2 for F8 (left) and �2 for O2 (right) from a patient with right temporal onset partial seizures (60-min epochs).

Fig. 4. Interictal Q2[F8-O2; 20-min epoch] (left) and preictal Q2 [F8-O2; 60-min epoch] (right) from a patient with right temporal onset seizures.

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the null hypothesis and accept the alternative hypothesisthat �adj � �remote (i.e., Q2 � 0). Fig. 6 shows the SPR fora collection of 44 interictal epochs of 20-min duration takenfrom 14 patients with TLE. Each point represents one 40-swindow. Fig. 7 is the SPR test for forty-four 60-min preictalepochs prior to seizure onset from this same cohort ofpatients, and Fig. 8 is the SPR for 24 epochs of 20-minduration taken from 6 nonepileptic subjects.

From Fig. 6 we see that we must reject the null hypoth-esis that the �2’s are the same for the adjacent and remoteelectrodes interictally and accept the alternative hypothesisthat Q2 � 0. In Fig. 7 we see that up to about 40 min priorto seizure onset, the �2’s for the adjacent electrodes aresignificantly greater than those for the ipsilateral remote

electrodes and the null hypothesis is below 5% significancelevel over that time period. Within about 40 min prior toseizure onset, the SPR increases, making rejection of thenull hypothesis no longer possible. Thus, the marginal pre-dictability of the electrodes adjacent to the site of ictal onsetis significantly greater than that of the ipsilateral occipitalelectrodes, until about 30–40 min prior to a seizure, whenthe marginal predictabilities take on similar values. Fig. 8displays the summed-rank test for epochs derived fromnonepileptic subjects. In marked contrast to the interictalepochs in patients with epilepsy, we cannot reject the nullhypothesis, and the �2’s for the ipsilateral temporal andoccipital electrodes are statistically the same.

These results are intriguing and strongly suggestive.

Fig. 5. �2 for F7 (left), �2 for O1 (middle), and Q2 (right) from a nonepileptic subject (20-min epochs).

Fig. 6. SPR for Q2 interictally between electrodes adjacent to and remote from yet ipsilateral to side of ictal onset.

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However, before claiming to be able to use this approach topredict seizures one must demonstrate, first, that the resultsare consistent across seizures for at least a subset of patientsand, second, that they are discriminative and can really beused to predict out-of-sample seizures. We are currentlystudying these issues. Preliminary results are encouraging.

Discussion

Our results and those of some others interested in seizureprediction indicate robust changes some tens of minutesbefore clinical or EEG evidence of a seizure begins. At firstglance this seems contrary to what most patients with sei-zure due to a localization-related epilepsy report. Indeed,Aicardi and Taylor (1998) viewed prodromes (“a long-termindication of a forthcoming attack”) as “mostly absent or ofsecondary importance.” Rajna (1997) reported that 42% ofpatients with a wide variety of different forms of epilepsyreport prodromes with an onset greater than 5 min beforeclinical seizures, some as much as hours before, with thegreatest occurrence in patients with localization-related ep-ilepsy. Thus, in contrast to Aicardi and Taylor (1998), theremay indeed be real value in understanding prodromes toepileptic seizures further.

Clinical and experimental evidence that more long-last-ing changes occur before clinical seizures does in fact exist.In aggregate they support the concept of a transition fromthe baseline interictal state, through some as yet poorlyunderstood preictal phase before overt seizure activity be-gins.

Preictal mood decline occurred in 13 of 27 epileptic

patients reported by Blanchet and Frommer (1986) in daysleading up to seizures with particularly striking changesoccurring in 6 of the 13. These changes were most apparentin the 24 h prior to a clinical event.

Sherwin (1978), using an acute feline penicillin model,reported complex changes in spike frequency over intervalsof 25 min preceding a clinical seizure. In acute cerebralinsults in humans leading to seizures with periodic lateral-ized epileptiform discharges (PLEDs) on EEG, clinical sei-zures are more likely to occur in patients where PLEDs areaccompanied by low-amplitude high-frequency discharges(“PLEDs-plus”) as described by Reiher et al. (1991). Thefast component discharges of PLEDs-plus are highly remi-niscent of the afterdischarges first described by Ralston(1958) as a consistent finding heralding interictal to ictaltransformation.

In ICEEG, recordings in humans (Lange et al., 1983)described systematic changes in the spatial organization ofinterictal spike patterns for at least 20 min before clinicalonset of seizures. This change was independent of absolutenumbers of interictal spikes and believed to be due to aninterhemispheric coupling of earlier intertemporal-lobe-in-dependent spike events.

Results of functional neuroimaging with single photonemission computed tomography (SPECT) scans as a mea-sure of regional cerebral blood flow (rCBF) show interictalhypoperfusion ipsilateral to the ictal onset zone transformedinto hyperperfusion ictally (Newton et al., 1992). Usingthermal diffusion flowmetry, Weinand et al. (1997) mea-sured surface CBF in 13 patients with medically refractorypartial seizures of temporal lobe origin. They found a grad-ual increase in CBF ipsilateral to the ictal onset zone be-

Fig. 7. SPR for Q2 preictally between electrodes adjacent to and remote from yet ipsilateral to side of ictal onset.

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ginning 20 min before and reaching a statistically significantdifference 10 min before seizure onset, when the valuesapproximated those in the contralateral temporal lobe. Al-though they postulated that changes in blood flow producedelectrographic and clinical seizure onset, it is more probablethat subtle alterations in neuronal activity of the type we andothers have detected led to a secondary increase in rCBF.Baumgartner et al. (1998) serendipitously obtained SPECTscans in two patients with temporal lobe epilepsy 11 and 12min before onset of clinical and EEG ictal activity. Whereinterictal SPECT showed hypoperfusion ipsilateral to theside of ictal origin, with preictal SPECT there was a signif-icant increase in rCBF in the epileptic temporal lobe.

Novak et al. (1999) reported changes in autonomic ner-vous system activity several minutes before recorded com-plex partial seizures as judged by time-frequency mappingof R–R intervals of the electrocardiogram. These occurredin seizures carefully selected to exclude the possibility ofphysical movement or changes in behavioral state account-ing for the changes. Subtle alterations in autonomic nervoussystem activity may be the underlying physiological basis

for the vague prodromal symptoms that some patients reportpreictally.

The studies reported in this paper differ methodologi-cally from most other work (Iasemidis et al., 1990; Casdagliet al., 1996; Lehnertz et al., 2001) in this field in two ways.First, we rely on data from scalp rather than intracranialrecordings, and, second, our indicators involve direct com-parisons between data from scalp recordings adjacent to andremote from the ictal onset zone. ICEEG and scalp EEGprobe different dynamic mechanisms. ICEEG records fromrelatively small regions of brain near the suspected ictalonset zone, limiting the extent to which it can probe spa-tially. Scalp EEG should not be thought of merely as signaldegraded ICEEG. Rather, scalp recordings are sensitive todifferent emergent effects in that there are anatomical struc-tures and propagation pathways on the length scale overwhich scalp electrodes record that are qualitatively distinctfrom the much smaller structures to which intracranial elec-trodes are sensitive. In addition, scalp recordings are, inmost instances, the only way to get EEG information fromregions of the brain that are distant from the ictal onset zone.

Fig. 8. SPR for Q2 for nonepileptic subjects.

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It is interesting that �2 is approximately the same be-tween temporal and occipital electrodes in normal subjects,compared to its distinct behavior in interictal and preictalepochs in patients with seizures. While it is unclear what thedetailed dynamical implications of these observations are,they suggest the following: first, the fact that �2 in normalsubjects is about the same between occipital and temporalelectrodes, but is not the same interictally in patients withseizures, suggests that there is an underlying persistent dif-ference in the dynamics of the epileptic brain that maypredispose it to the neuronal recruitment process that givesrise to a seizure. Second, the systematic differences betweeninterictal and preictal epochs of Q2 in the patient withseizures supports the notion that there is a recruitmentprocess that takes place several tens of minutes prior to aseizure. Finally, it is interesting to note that, although thereis consistency in the behavior of Q2 between interictal andpreictal epochs (Q2(preictal) � Q2(interictal), the way inwhich the �2’s for occipital and temporal electrodes behavevaries from seizure to seizure. In some cases Q2 decreasespreictally because �2 for the focal channel decreases, whilein other cases Q2 decreases preictally because �2 for theoccipital channel increases as a seizure is approached. Thissuggests that while there may be a neuronal recruitmentprocess that gives rise to most seizures in patients with thissyndrome, the way in which this process proceeds dynam-ically may be quite different from one seizure to the next.

The emerging pattern from this body of work is that thereis an extended (several tens of minutes) preictal period inlocalization-related epilepsy. Our contributions are, first, todemonstrate that there are consistent changes in this preictalperiod that are observable on scalp EEG and, second, tosuggest possible general dynamical features that may un-derlie these changes. These observations suggest severalscenarios for a more detailed explanation of the dynamics ofictal genesis. First, there appears to be a relation betweenthe hypoperfusion of the ictal onset region interictally(Newton et al., 1992) and the observation that �2 is greaterinterictally in scalp electrodes near the seizure focus com-pared to more remote electrodes. On the other hand, theroute to ictal onset, associated with the increased perfusionof the ictal region, may be accomplished in several differentways. Routes to seizure onset can involve a diminution of �2

for focal electrodes or an increase in �2 for more remoteelectrodes. In the former case, we speculate that seizureonset is the result of a diminution of the neuronal controldue to reduced inhibitory influences near the site of ictalonset. In the latter case, we propose that seizure onset maybe a consequence of simplified dynamics in areas remotefrom the ictal onset zone and caused by an alteration infeedback between regions remote from and adjacent to theictal onset zone. While the precise nature of this feedback isunclear, it is clear from Figs. 6 and 7 that absence of ictaldischarge depends not only on neuronal dynamics local tothe ictal zone, but also on the behavior of neuronal dynam-ics in more remote regions of the brain. Spencer (2002)

introduced the concept of a network model of human epi-lepsy and cited substantial clinical evidence to support anumber of different networks in localization-related epi-lepsy in humans. She defines a network as “a functionallyand anatomically connected, bilaterally connected set ofcortical and subcortical brain structures and regions inwhich activity in any one part affects activity in all theothers . . . Implicit in this idea is that seizures may entrainthis large neural network from any given part, such that itbecomes irrelevant to discuss the ‘onset’ of seizure in anyspecific part of the network.” It is our opinion that nonlineardynamical studies of scalp EEG offer a particular usefulwindow into understanding how regions remote from whatwould have been considered in traditional terms the ictalonset zone may lead to the onset of a seizure. We wouldmodify Spencer’s view slightly (at the risk of appearingtautological) and suggest that some phenomenon, in ad-vance of what at least currently we consider the onset of aseizure and happening anywhere in the network, may triggerthe actual clinical event itself. These speculations havetestable consequences in that we should see different pat-terns of seizure propagation correlated with the differentroutes to seizure onset (decreasing �2 focally and/or increas-ing �2 remotely) that we have described. For example,insofar as an increase in �2 in a remote electrode during apreictal period contributes to ictal onset, one might expectthat in those cases ictal propagation from the ictal onsetzone into remote areas of the brain would be faster than forcases in which �2 decreases focally during the preictalperiod, but does not increase remotely. In normal subjectsthere is no significant difference in �2 between temporal andoccipital electrodes since there is no need for a simplifica-tion of macroscopic neuronal dynamics anywhere in thebrain, if there is no threat of localized seizure onset.

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