epilepsia 2007 plummer

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Epilepsia, **(*):1–18, 2007 doi: 10.1111/j.1528-1167.2007.01381.x CRITICAL REVIEWS AND INVITED COMMENTARY EEG source localization in focal epilepsy: Where are we now? * Chris Plummer, §A. Simon Harvey, and * Mark Cook * Centre for Clinical Neurosciences and Neurological Research, St Vincent’s Hospital, Fitzroy, Victoria, Australia; Departments of Medicine and Paediatrics, University of Melbourne, Parkville, Victoria, Australia; and §Department of Neurology, Royal Children’s Hospital, Parkville, Victoria, Australia SUMMARY Electroencephalographic source localization (ESL) by noninvasive means is an area of renewed inter- est in clinical epileptology. This has been driven by innovations in the computer-assisted modeling of dipolar and distributed sources for the investiga- tion of focal epilepsy; a process fueled by the ever- increasing computational power available to re- searchers for the analysis of scalp EEG recordings. However, demonstration of the validity and clini- cal utility of these mathematically derived source modeling techniques has struggled to keep pace. This review evaluates the current clinical “fitness" of ESL as applied to the focal epilepsies by examin- ing some of the key studies performed in the field, with emphasis given to clinical work published in the last five years. In doing so, we discuss why ESL techniques have not made an impact on rou- tine epilepsy practice, underlining some of the cur- rent problems and controversies in the field. We conclude by examining where ESL currently sits alongside magnetoencephalography and combined EEG-functional magnetic resonance imaging in the investigation of focal epilepsy. KEY WORDS: Source modeling, Dipole, Dis- tributed, Electroencephalography, Magnetoen- cephalography, Functional magnetic resonance imaging. CONCEPTS AND CONTROVERSIES In the last five years, research in the field of electroen- cephalographic source localization (ESL) produced more than 150 scientific papers on computer-assisted mathemat- ical techniques for dipolar and distributed source model- ing. By comparison, less than half of this number of pub- lications addressed the clinical validation of such tech- niques for the investigation of focal epilepsy. Most clin- ical studies featured less than 20 subjects and few were conducted prospectively. Such an imbalance might be ex- plained away by the relative efficiency with which ESL simulation studies yield publishable results, particularly in the view of the advancing computational power and data storage capacity of the modern PC processor. However, this explanation falls short of addressing the confusion, even cynicism, among neurologists and neurosurgeons as Accepted October 9, 2007; Online Early publication xxxxxx Address correspondence to Chris Plummer, Centre for Clinical Neu- rosciences and Neurological Research, St Vincent’s Hospital, 5th Floor Daly Wing, 35 Victoria Parade, Fitzroy, Victoria, Australia 3065. E-mail: [email protected] Blackwell Publishing, Inc. C 2007 International League Against Epilepsy to the clinical validity and utility of these mathemati- cally complex, often nonintuitive, modeling techniques. As such, will ESL ever realize a place in the routine work- up of patients with focal epilepsy? If so, what is the cur- rent level of evidence and what additional evidence is required for ESL to achieve this status? Has ESL been swept aside in recent years by the newer neuroimaging modalities of magnetoencephalography (MEG) and com- bined EEG-functional magnetic resonance imaging (EEG- fMRI)? The fundamentals of ESL As a discipline that aims to localize the sources of elec- tric currents within the brain that give rise to recordable potential fields at the scalp (Fig. 1), ESL is almost as old as the science of EEG itself (Jayakar et al., 1991). Since the days of paper analog recordings, ESL in the generic sense has been “geared up” in the last few decades by computer-assisted source modeling techniques on the back of digital EEG technology. Computer-assisted ESL (we now limit “ESL” to this context) brings with it a new set of challenges to the same goal that faced first generation elec- troencephalographers; that is the noninvasive localization 1

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  • Epilepsia, **(*):118, 2007doi: 10.1111/j.1528-1167.2007.01381.x

    CRITICAL REVIEWS AND INVITED COMMENTARY

    EEG source localization in focal epilepsy: Where arewe now?

    Chris Plummer, A. Simon Harvey, and Mark CookCentre for Clinical Neurosciences and Neurological Research, St Vincents Hospital, Fitzroy, Victoria, Australia;

    Departments of Medicine and Paediatrics, University of Melbourne, Parkville, Victoria, Australia;and Department of Neurology, Royal Childrens Hospital, Parkville, Victoria, Australia

    SUMMARYElectroencephalographic source localization (ESL)by noninvasive means is an area of renewed inter-est in clinical epileptology. This has been driven byinnovations in the computer-assisted modeling ofdipolar and distributed sources for the investiga-tion of focal epilepsy; a process fueled by the ever-increasing computational power available to re-searchers for the analysis of scalp EEG recordings.However, demonstration of the validity and clini-cal utility of these mathematically derived sourcemodeling techniques has struggled to keep pace.This review evaluates the current clinical fitness"of ESL as applied to the focal epilepsies by examin-

    ing some of the key studies performed in the field,with emphasis given to clinical work published inthe last five years. In doing so, we discuss whyESL techniques have not made an impact on rou-tine epilepsy practice, underlining some of the cur-rent problems and controversies in the field. Weconclude by examining where ESL currently sitsalongside magnetoencephalography and combinedEEG-functional magnetic resonance imaging in theinvestigation of focal epilepsy.KEY WORDS: Source modeling, Dipole, Dis-tributed, Electroencephalography, Magnetoen-cephalography, Functional magnetic resonanceimaging.

    CONCEPTS AND CONTROVERSIESIn the last five years, research in the field of electroen-

    cephalographic source localization (ESL) produced morethan 150 scientific papers on computer-assisted mathemat-ical techniques for dipolar and distributed source model-ing. By comparison, less than half of this number of pub-lications addressed the clinical validation of such tech-niques for the investigation of focal epilepsy. Most clin-ical studies featured less than 20 subjects and few wereconducted prospectively. Such an imbalance might be ex-plained away by the relative efficiency with which ESLsimulation studies yield publishable results, particularly inthe view of the advancing computational power and datastorage capacity of the modern PC processor. However,this explanation falls short of addressing the confusion,even cynicism, among neurologists and neurosurgeons as

    Accepted October 9, 2007; Online Early publication xxxxxxAddress correspondence to Chris Plummer, Centre for Clinical Neu-

    rosciences and Neurological Research, St Vincents Hospital, 5th FloorDaly Wing, 35 Victoria Parade, Fitzroy, Victoria, Australia 3065. E-mail:[email protected]

    Blackwell Publishing, Inc.C 2007 International League Against Epilepsy

    to the clinical validity and utility of these mathemati-cally complex, often nonintuitive, modeling techniques. Assuch, will ESL ever realize a place in the routine work-up of patients with focal epilepsy? If so, what is the cur-rent level of evidence and what additional evidence isrequired for ESL to achieve this status? Has ESL beenswept aside in recent years by the newer neuroimagingmodalities of magnetoencephalography (MEG) and com-bined EEG-functional magnetic resonance imaging (EEG-fMRI)?

    The fundamentals of ESLAs a discipline that aims to localize the sources of elec-

    tric currents within the brain that give rise to recordablepotential fields at the scalp (Fig. 1), ESL is almost as oldas the science of EEG itself (Jayakar et al., 1991). Sincethe days of paper analog recordings, ESL in the genericsense has been geared up in the last few decades bycomputer-assisted source modeling techniques on the backof digital EEG technology. Computer-assisted ESL (wenow limit ESL to this context) brings with it a new set ofchallenges to the same goal that faced first generation elec-troencephalographers; that is the noninvasive localization

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    C. Plummer et al.

    Figure 1.(A) Scalp recorded 19-channel EEG using Common Average Reference from a patient with BFEC. Values at theextreme right of channel waveforms correspond to surface potential points (in microVolts) at the 24.22 mseclatency marker (midway between onset and offset interval markers, asterisked). As is customary, negative valuesindicate deflections above the zero potential line. The MGFP curve for the BFEC sharp wave complex is shown.Note the earlier, small surface negative frontal sharp wave (at onset marker, maximal at F3) and the later, largersurface negative temporal sharp wave (at offset marker, maximal at T5). (B) Butterfly plot showing superimposedwaveforms for the 19 channels. (C) Isopotential field plots for 36-msec period following peak of early spike wavecomponent of same BFEC discharge. Note the polarity inversion of the dipolar field that occurs across the courseof the discharge. The left frontal field is initially surface negative (blue) and later surface positive (red), while the lefttemporal field is initially surface positive (red) and later surface negative (blue). The isopotential lines demonstratethat the later surface negative temporal field has a broader distribution than the earlier, more concentric, negativefrontal field. Abbreviations: BFEC (Benign Focal Epilepsy of Childhood), MGFP (Mean Global Field Power).Epilepsia C ILAE

    of epileptogenic networks in the patient presenting withepilepsy.

    Two fundamental problems exist in the practice ofESLforward and inverse. The forward problem is solvedby specifying a set of conditions (compartments, sur-faces, conductivities) for the head model, also referred toas the volume conductor or forward model. The forwardmodel by analogy is the stage on which the source, orsource network, performs, its projected (lead) field pass-ing through modeled compartments and tissue interfacesto reach the recording electrodes. It is the set of condi-tions specified to the forward problem that distinguishesone forward model from another. Forward models range

    from simple (a single spherical shell models the brain sur-face) to complex (a four-layered realistic model, its com-partments segmented from the patients MRI scan, mod-els the brain, cerebrospinal fluid, skull, and scalp sur-faces). Spherical shell and realistic models are the two ver-sions of forward modeling used in ESL today (Fig. 2).The former vary in complexity from single shell to mul-tiple (two, three, or four) overlapping shell models, andthe latter are sub-divided into boundary and finite elementmethod (BEM, FEM) models. We touch on the applicationof these models in the next section. For a specific elec-trical source, the forward model will enable the computa-tion of a specific potential field at its surface (Wilson &

    Epilepsia, **(*):118, 2007doi: 10.1111/j.1528-1167.2007.01381.x

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    EEG Source Localization in Focal Epilepsy

    Figure 2.The evolution of forward modeling in ESL. Spherical shell models range in complexity from one to four overlappingshell surfaces to model the head as a volume conductor (single shell- brain, 2 shell- brain, skull, 3 shell- brain, skull, skin,4 shell- brain, CSF, skull, skin). Realistic head models (BEM and FEM) are so called because they better approximatethe shape of the human brain than do shell models. This is particularly the case at the brains deeper, inferior surfaces,as illustrated here with a 4-shell model projected over a digitally reconstructed cortex from an averaged MRI scan.The BEM models are composed of overlapping, two-dimensional, triangulated mesh layers (or boundaries), eachlayer having been computer generated from segmented T1-weighted MRI surfaces (scalp, skull, CSF, and cortex).Different compartments are usually given different conductivity values, but conductivity within each compartment isassumed to be isotropic and homogenous. In contrast, the FEM models are composed of multiple, three-dimensional,solid tetrahedra, a property that allows conductivity values to vary within each compartment. This means that tissueanisotropy can be factored into algorithms that solve the forward problem. As distinct from the 4-shell model, notehow well the FEM captures the shape of the brain in the modeling of its innermost compartment. Abbreviations: BEM(Boundary Element Method), CSF (cerebrospinal fluid), FEM (Finite Element Method), MRI (Magnetic ResonanceImage).Epilepsia C ILAE

    Bayley, 1950). Thus, the forward problem will give aunique solution.

    The inverse problem, by contrast, has no unique solu-tion. That is, an infinite number of source permutationscan, in theory, explain a specific potential field recordedat the surface (Helmholtz, 1853). This is the problem theelectroencephalographer attempts to solve in routine clin-ical practice, traditionally with a minds eye rendering ofcandidate sources drawn from the various EEG montagedigital displays. In practice, the experienced electroen-cephalographer constrains the infinite possible solutionsto the inverse problem by applying their working knowl-edge of epileptogenesis in the focal epilepsy syndromes to

    the patients clinical picture, supplemented perhaps by in-formation from anatomical or functional imaging studies.Similarly in ESL, the inverse problem is made soluble bythe incorporation of mathematical constraints into inversemodeling algorithms.

    Just as volume compartment and boundary condi-tions distinguish one forward model from the next, con-straint conditions distinguish one inverse model fromthe next. The two major inverse modeling approachesare the dipolar and distributed modeling methods. Themathematics of inverse modeling can be quite complexand it is not the purpose of this paper to summarizethe algebraic pros and cons of the several dipolar and

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    Figure 3.(A) ESL example of a Moving dipole inverse model used with an FEM forward model based on the same BFEC sharpwave complex depicted in Figure 1. Each dipole is fitted to a single time point at successive 4-msec intervals acrossthat part of the interictal discharge shown in Figures 1A, 1B. Relative dipole size is in proportion to dipole strengthor amplitude. Note that each color-coded dipole symbol is made up of a spherical end directed toward the surfacenegative cortex and a straightened tip directed toward the surface positive cortex. Hence, the earlier, smaller surfacenegative frontal sharp wave seen in the BFEC discharge is represented by the smaller, darker green dipoles, while thelarger, lighter green dipoles represent the upswing phase of the larger, later surface negative temporal sharp wave.Also note that an example of a confidence ellipsoid is shown attached to one of the larger dipoles (asterisked). Itis worth pointing out that different types of dipole symbols are used in the ESL literature, but investigators do notroutinely spell out surface negative and surface positive aspects of such dipole symbols. (B) LORETA distributed inversemodel used with a BEM forward model. Note the relatively diffuse distribution of the current density map spanningthe left central sulcal region. Broad, blurred ESL solutions are quite typical for the LORETA method. Brighter signalintensities indicate higher current density values (scaled as microamperes per square millimeter). Orthogonal axesare shown (+x left, +y posterior, +z upward). Appreciate that the single time point chosen for display (24.22msec) approximates the halfway point of the upswing phase of the surface negative discharge, a point which, on currentevidence, appears to most reliably reflect the state of the corresponding intracranial potential field. Abbreviations:BEM (boundary element method), BFEC (Benign Focal Epilepsy of Childhood), ESL (EEG source localization), FEM(finite element method), LORETA (Low Resolution Electromagnetic Tomography).Epilepsia C ILAE

    distributed models available in ESLfor reviews see (Dar-vas et al., 2004; Michel et al., 2004a). What shouldbe stressed though is that dipolar methods are over-determined (Fuchs et al., 1999) in the sense that the inves-tigator preselects one, two, or three (rarely more) dipolesto apply to the inverse algorithm in question. This meansthere are far more data sampling points (viz. electrodes)than there are dipole parameters in determining the ESLsolution.

    Two of the most commonly used inverse algorithms inESL are the moving and rotating dipole methods. The mov-ing algorithm constrains the dipole to instants in time (suc-cessive 4 msec time instants in a 256 Hz recording are usu-ally solved); but frees it in space to assume a location, ori-entation, and strength to best explain the measured EEGdata at each time instant (Fig. 3A). The rotating algorithmconstrains the dipole to a location in space; but frees it toassume an orientation and strength to explain the variance

    in the measured data across any time interval (Fuchs et al.,2004a).

    In contrast, distributed methods are under-determined(Fuchs et al., 1999). That is, there are far fewer samplingpoints than possible ESL solutions. This is because, un-like dipole modeling strategies, no assumption is madeon the number of dipoles used to solve the inverse prob-lem. Instead, the working premise is that multiple sourcesmay be simultaneously active across multiple locations ata given instant in time. The predefined solution space (beit the whole brain volume or just the cortical volume) issplit into multiple points, each point representing a mini-dipole, fixed in space but free to assume any orientationand strength. Due to the enormous number of permuta-tions that stem from such mini-dipole networks, all offer-ing a theoretically plausible explanation for the measuredEEG signal, postprocessing constraints need to be appliedto achieve a unique ESL solution.

    Epilepsia, **(*):118, 2007doi: 10.1111/j.1528-1167.2007.01381.x

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    EEG Source Localization in Focal Epilepsy

    Low-resolution electromagnetic tomography(LORETA) is one of the more familiar distributedmodeling algorithms used in ESL (Fig. 3B). LORETAapplies a modeling constraint based on the idea thatneighboring neuronal populations are more likely (thannonneighboring ones) to undergo synchronous depolariza-tion during a spontaneous discharge or an evoked response(Pascual-Marqui et al., 1994). LORETA modeling tends togenerate broad, smoothed ESL solutions as neighborhoodsources are model term conditioned to assume similarstrengths. As discussed later, such assumptions may notalways be in touch with electrophysiological reality.

    It is often misunderstood that the inverse and forwardproblems are interdependent. They are only interdependentin the sense that both are required to generate an ESL so-lution. However, the mathematical algorithms specified toeach problem are independently set (Scherg et al., 1999).This is important to appreciate because any ESL solutioncan be reached when any inverse model is coupled withany forward model. The two major challenges then, lie in(a) deciding on which of the available forward and inversemodels are the most appropriate to apply, and (b) trans-lating the theoretical impact governed by the choice of aparticular forwardinverse modeling set-up into terms thatdefine the clinical impact of such a choice on the patientsdiagnosis and management. Scant attention has been givento the latter in the ESL literature to date. Most clinical stud-ies preselect a particular forwardinverse modeling combi-nation (shell or realistic-dipolar or distributed) and test itsperformance in a particular clinical setting either directly,using simultaneously acquired intracranial EEG, or morecommonly indirectly, using estimates of concordance withother functional and anatomical studies.

    On the forward problem and its problemsThe relative disconnect at the technicalclinical inter-

    face of ESL research is exemplified by the forward prob-lem. The digital reconstruction of realistic forward mod-els that predict the impact of volume conduction on thegeneration of scalp potentials remains a central theme inbiophysics EEG research. But on this point, Niedermeyerreminds us of Jaspers observation 40 years ago that prop-agation along conducting pathways represents the most im-portant mechanism of signal spread (especially as regardsepileptiform signals) (Jasper, 1969), himself warning thatexcessive emphasis on volume conduction and total re-liance on biophysics are not the answer to EEG signalanalysis (Niedermeyer, 2005). There is little doubt thatthe modeling of basal source activity, as in temporal lobeepilepsy, is optimized with the use of realistic head mod-els that more accurately delineate the nonspherical, infe-rior aspects of the brain compared to overlapping shellmodels (Ebersole, 1997a). The latter give dipole locationerrors of up to 30 mm in the rostral directionsphericalshell models commonly mislocalize known mesial tempo-

    ral lobe source activity to the frontal lobe (Cuffin, 1996;Roth et al., 1997). Errors in dipole orientation were alsonoted to increase when spherical models were used inplace of realistic models in a more recent simulation study(Crouzeix et al., 1999). While these observations are im-portant, it should be appreciated that the effects of signalpropagation (primarily via cortico-cortical pathways) onscalp EEG voltage topography are not factored into equa-tions designed to solve the forward problem.

    It is probably under-emphasized that in terms of present-day clinical utility, the property that differentiates spher-ical from realistic head models simply relates to modelshape, rather than to any more advanced feature (Fuchs etal., 2002). Spherical models conform to frontal and pari-etal brain convexities reasonably well but are found want-ing when it comes to the modeling of infero-occipital,infero- and mesial temporal, and orbitofrontal brain sur-faces (Ebersole, 2003a), regions that commonly playhost to the epileptogenic zone in focal epilepsy. Researchefforts aimed at further advancing realistic models, suchthat they are brought closer to reality, remain hamstrungby three factors.

    The first is the extra computational demand that com-plicates the integration of tissue anisotropy parametersinto the forward modeling algorithm. While finite elementmethods aim to satisfy the more physiological anisotropic,heterogeneous conduction that takes place within tissuecompartments and across tissue boundaries (Fuchs et al.,2007); in practice, most realistic models (viz. boundary el-ement methods) assume homogenous, isotropic conductionwithin compartments and limit conductivity variability tosurface boundaries alone.

    The second factor is the absence of agreed-upon tissueconductivity values for the various tissue compartments inthe human head. For example, the wide range of valuespublished for the skull-to-brain conductivity ratio in hu-mans is at least partly due to inherent discrepancies be-tween in vitro and in vivo based findings (Nunez & Srini-vasan, 2006a). Clinical studies often apply conductivityvalues for the brain, skull, and scalp that are based on anin vitro study published 40 years ago (Geddes & Baker,1967). In any case, a more recent in vivo study suggeststhat the inter-individual variability of these properties maylimit the generalizability of tissue conductivity values forrealistic models (Ha et al., 2003). Moreover, while it hasbeen theorized that the brainskull interface dampens thevoltage of a dipole point to one-eighth of its strength, itshould be kept in mind that skull conductivity actuallyimproves with increasing skull thickness due to the ac-companying increase in the ratio of cancellous to corticalbonemarrow conductivity being higher than that of corti-cal bone (Nunez & Srinivasan, 2006a). Thus, the individ-ualization of forward modeling would expectedly dependon regional nuances in individual skull thickness and cra-nial contouring relative to the brain, the latter influencing

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    electrode distance and electrode orientation relative to thecortical surface (Binnie et al., 1982).

    The third problem that needs to be resolved is the clin-ical relevance of incorporating brain tissue anisotropy intoforward modeling calculations via finite element meth-ods. Average white matter resistivity is approximately dou-ble that of gray matter, largely by virtue of the multi-directional nature of white matter fiber tracts (Nunez &Srinivasan, 2006a). While diffusion tensor imaging (DTI)techniques are beginning to avail quantification of whitematter anisotropy (Mori & van Zijl, 2002), the relative clin-ical impact of such a parameter on volume conduction (vsbrain-skull and scalp-air interface effects) remains unqual-ified. Although speculative, anisotropic modeling of braintissue may offer some much needed insight into the bio-physical properties of interictal and ictal source propaga-tion occurring across cortical regions. The immediate rel-evance of this becomes apparent when one considers thatapproximately 98% of pyramidal cell input in humans isvia cortico-cortical connections (Braitenberg, 1978).

    On the inverse problem and its misconceptionsJust as forward models vary in complexity from single

    shell models to multicompartment realistic models, inversedipolar models range from single fixed dipoles fitted to sin-gle time points (as with fixed and moving dipoles) to mul-tiple dipoles that overlap in three-dimensional space acrosstime intervals (as with rotating and regional spatiotempo-ral dipoles). Likewise, distributed models can be broadlyclassified into those that use linear mathematics (as withthe so called minimum norm, depth-weighted minimumnorm, and LORETA models) and those that use nonlinearmathematics (as with L1 norm methods). However, ratherthan presenting a didactic discussion on the variety of in-verse algorithms, both dipolar and distributed, available inESL today (some of which we discuss further in the nextsection), we will address some of the key concepts in in-verse modeling as a series of common misconceptions.

    (A) Dipole models provide point-like anatomicalsolutions for spike and seizure localization

    It is tempting to interpret dipoles as point sources of in-terictal or ictal activity when they are pitched, as is oftendone in publications, over a coregistered image from thepatients MRI scan. This is especially so when ESL stud-ies not uncommonly infer that a dipoles x, y, z locationin space is the sine qua non of a dipole models accuracy.This misconception is understandable given the millimetermargins of error that are often cited for dipole positionsin simulation studies, particularly in the biophysics litera-ture. As Ebersole has repeatedly emphasized, a dipole isnot a discrete anatomical construct but a theoretical con-cept on which the modeling of relatively large segmentsof synchronously discharging cortex is based (Ebersole &Hawes-Ebersole, 2007). The threshold cortical area for aspike to be seen by the scalp electrodes is 10 cm2 (Tao

    et al., 2005). The orientation of the dipole is just as, ifnot more, informative of the behavior of a putative source.The state of a dipoles orientation within milliseconds ofspike or seizure onset can shed light on patterns of cortico-cortical propagation, a phenomenon not easily appreciatedfrom the traditional visual inspection of the EEG waveformalone (Ebersole, 1994).

    (B) The dipole marks the center of mass of the sourceThis is an oft-quoted phrase in the ESL literature. Unfor-

    tunately, as a definition it is somewhat vague and, as Got-man points out, there is actually no proof for it despite itslongevity as a concept (Gotman, 2003). Rather than visual-izing a dipole as the center of a mass of equivalent current,simultaneous surface-depth recordings suggest that, for agiven time point or time interval, the dipole most reliablymodels the maximum potential of a source whose intracere-bral field is often quite extensive (Merlet & Gotman, 1999)and whose strength, location, and orientation can changequite rapidly across the time course of an epileptiform dis-charge.

    (C) As artificial concepts, dipole and distributed modelscarry little electrophysiological relevance

    Electroencephalograph literally means electrical brainpicture electro-encephalo-gramma being the Greekroots (Knott, 1985). Our present understanding of EEG sig-nal generation is actually based on the electrophysiologicaltheory that the EEG waveform is the product of a myriadof dipoles, or dipolar configurations, that flux in polaritywithin the cortical space (Brazier, 1949; Gloor, 1985; Eber-sole, 2003b). The electromotive force behind dipolar fieldgeneration is the resting membrane potential of the pyra-midal neuron. Following its excitation at the postsynap-tic membrane, the pyramidal cell experiences a progressivewave of depolarization along the length of the axon. Pas-sive loops of extracellular current are set up to completethe local circuit (Buzsaki et al., 2003). It is the linear sum-mation of the extracellular components of these pyramidalcell microcircuits, positive and negative, that configures thesource and its projection to the scalp as a recordable poten-tial field. It is useful to visualize this extended source activ-ity through the conceptual lens of Gloors solid angle the-ory (Gloor, 1985). Perhaps the most common error in EEGreading is to see the electrode that registers the peak volt-age of an epileptiform discharge as the one that lies closestto the source (Ebersole, 2000). Gloors theory reminds usthat it is the cortical configuration (area and orientation)of the source in relation to the recording electrode, ratherthan the source-to-electrode distance, that determines EEGsurface polarity. With this is mind, it can be appreciatedthat both surface polarity maxima (positive and negative)of the potential field carry useful localizing information(Fig. 1C). For instance, it is typically the contralateralsurface-positive, and not the ipsilateral surface-negative,potential field maximum that better delineates the origin

    Epilepsia, **(*):118, 2007doi: 10.1111/j.1528-1167.2007.01381.x

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    EEG Source Localization in Focal Epilepsy

    and propagation of ictal or interictal source activity inmesial versus lateral temporal lobe epilepsy (Ebersole &Wade, 1990).

    In electrophysiological terms, it is important to keep thespatial limitations of dipole modeling in perspective. Onecubic millimeter of human neocortex contains around 105

    neurons and 109 synapses. One scalp electrode is estimatedto record the synchronized and aligned space-averaged po-tentials of around 108 to 109 neurons (Nunez & Srinivasan,2006a). One dipole models upwards of around 10 cm2 ofcortical tissue. This is why it is important not to regarddipole solutions as point-like millimeter (let alone sub-millimeter) indices of abnormally discharging cortex. Bythe same token, such a logarithmic leap in spatial dimen-sion has led Nunez to propose that, should there be a ma-jor shift in our future understanding of epileptogenesis atthe micro-scalar level (cellular and subcellular), the elec-trophysiological relevance of dipole modeling theory at themacro-scalar level (lobar and sublobar) will likely remainintact (Nunez & Srinivasan, 2006a). It is rather the nexttierthe application of macro-scalar theory, in the formof ESL, to routine epilepsy patient work-upthat needsmore rigorous proof of concept at this point in time.

    (D) The surface negative peak of the highest amplitudespike from the scalp EEG recording gives the mostreliable ESL result

    This is both partly true and false. It is technically truebecause dipole and distributed modeling solutions are moststable when the signal to noise ratio (SNR) is highest, as isusually the case at the spike peak. The stability of an ESLsolution, or more specifically, the parameters that define it(location, orientation, strength), relates to its reproducibil-ity and, pending the suitability of the forwardinverse mod-eling set-up, to its capacity to explain the signal varianceat the scalp electrodes. Along these lines, a recently intro-duced strategy to help quantify the probability of an ESLsolution is the confidence ellipsoid (CE) volume calcula-tion (Fuchs et al., 2004b). Dipoles fitted with CE volumesare, by definition, free to roam within the confines of theellipsoid space without its inverse fit parameters impact-ing on the forward fit solution beyond the level of noiseattached to the solution subspace (Fig.3A). In other words,the smaller the CE volume, the greater is the probabilitythat the dipole resides at the fit location for a given timepoint or time interval. An inverse relationship between theCE volume and the SNR has been demonstrated in bothsimulation (Fuchs et al., 2004b) and clinical (Plummer etal., 2007) studies. Thus, small CE volumes tend to occur inthe vicinity of the spikes peak where the SNR is typicallyhigher.

    The above tenet is, however, misleading in terms of theprobability that the ESL result actually models the originalinterictal or ictal source. It has become increasingly recog-nized that ESL results based on spike peak activity, an ap-

    proach that is still seen in clinical studies, should be inter-preted with caution. This is because simultaneous surface-depth recordings reveal, perhaps not surprisingly, that it isthe earlier component of the epileptiform discharge at thescalp, which most closely matches the location and field ofthe source as suggested by the corresponding intracranialEEG activity (Fig. 1A, 1B). At the scalp recorded spikepeak, the signal is often well removed from the originalsource due to the effects of cortico-cortical propagation.The problem then lies with the accurate modeling of ear-lier phase interictal or ictal activity when the signal is oftenburied in noise. Scherg has emphasized that source activ-ity onset is best demarcated with a higher low-filter set-ting, recommending a 210 Hz frequency threshold rangeinstead of the more traditional 0.51 Hz cut-off (Scherget al., 1999). The effect is to minimize the contribution ofslower frequencies that are less likely to figure in the earli-est source activity. In a similar vein, he stresses the impor-tance of using a forward noise filter for ESL, rather than azero-phase shift filter, as the latter tends to artificially blursignal onset and offset.

    Assuming technically satisfactory EEG signal acquisi-tion, the SNR is commonly optimized by averaging sin-gle events. However, averaging carries the inherent riskof mislocalizing single events if the latter are not trulymonomorphic (Braga et al., 2002; Chitoku et al., 2003).Various methods, such as phase coherence and global fieldpower correlation (Lehmann, 1987), have been used to helppool identical discharges for averaging purposes. Whileaveraging can improve the localizability of the earliercomponents of focal epileptiform discharges (to the pointwhere it tends to be done routinely in research), few stud-ies have rigorously examined the clinical impact of singleversus averaged event selection in dipolar and distributedmodeling.

    (E) ESL is too cumbersome to perform. Too manyelectrodes, too much computer knowledge, too manydifficulties with image coregistration, and too many timedemands make it impractical for routine use in the clinicalsetting

    There remains no fixed agreement on the minimumnumber of scalp electrodes required for clinically usefulESL in focal epilepsy. While high-density electrode ar-rays can improve the spatial resolution of surface EEGsignal topography, and thus facilitate the task of distin-guishing source origin from source propagation, there isthe penalty of having to measure and fix hundreds of elec-trodes to the scalp. On theoretical estimates, the mini-mal number of scalp electrodes required for optimal EEGspatial resolution, the so-called Nyquist criterion, lies be-tween 100 and 200 (Gevins, 1993; Srinivasan et al., 1996).Electrode caps are not an ideal answer to this problem aselectrode-scalp contacts can be unreliable and their usefor long-term EEG monitoring is impractical. The loading

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    of scalp electrodes over putative cortical foci, as with theuse of inferior temporal arrays in temporal lobe epilepsy,can provide clinically valid ESL results based on intracra-nial EEG localization (Ebersole, 2003a). Dipole simula-tion work has also demonstrated that nonuniform samplingwith 1010 scalp positions in the region of interest and1020 positions elsewhere can provide reliable estimatesof source characteristics (Benar & Gotman, 2001). A morerecent study in a group of 14 patients with refractory fo-cal epilepsy and Engel class 1 surgical outcomes showedthat ESL accuracy, indexed by the distance from the near-est surgical margin to the location of a single fit inversemodel, improved by around 2 cm from a 31 to a 63 elec-trode set-up, with little change from a 63 to a 123 electrodeset-up (Lantz et al., 2003a). While source orientation wasnot considered and single-spike peaks were modeled, thisis, perhaps surprisingly, the first study to have systemati-cally examined this issue in a well-defined patient group.The question of optimum scalp electrode number for ESLmay be settled by default with the future development ofquicker methods that reliably fix high-density electrode ar-rays to the scalp. Until then, more studies in the manner ofLantz et al. are needed.

    Coregistration problems are more readily overcome withthe recent availability of MRI compatible electrodes andthe ability to perform less artifact-laden EEG recordingsin the MRI scanner. Newly developed coregistration meth-ods, based on the use of mutual three-dimensional vir-tual landmarks from patient MRI datasets, show promise(Fuchs et al., 2007) but await clinical validation.

    The computer technology, on which current-generationESL relies, has become more accessible to clinicians in thelast five years due to improvements in the user interface ofsoftware operating systems.

    (F) Distributed models display sources as current densityfield maps that are closer to reality than dipolemodeled sources

    This, understandably, is not an uncommon misconcep-tion. The distributed-ness of a distributed algorithms so-lution is generally not the direct representation of the po-tential field of the actual source. This only holds if the dis-tributed model is perfectly correct, which is virtually neverthe case. In fact, much of the apparent field effect re-sults from the distributed algorithms inexactness in model-ing the source. Some of this modeling error can be limitedby preconstraining the ESL solution to anatomically mean-ingful boundaries, such as the cortex, a benefit carried bydistributed over dipolar modeling methods (Wagner et al.,2001). However, by virtue of their under-determined na-ture, distributed algorithms are computationally more de-manding such that, for most present applications, ESL so-lutions can only be calculated for time instants. This meansthat it is difficult to appreciate the relative timing of over-lapping source components contributing to the modeled

    cortical activity across the early spike interval (Scherg etal., 1999). Also, for distributed ESL solutions to be suffi-ciently spatially resolved, current density thresholds are setwhich, not unlike fMRI signal thresholds, are typically ar-bitrary. Hence, the more established anatomical pathwaysof interictal and ictal discharge propagation are not fac-tored into current density threshold settings.

    CLINICAL STUDIES INDIPOLAR AND

    DISTRIBUTED ESLDespite the clinically based research efforts in ESL by

    Ebersole and others over the last two decades, Krauss andWebber still have it that digital EEG has not significantlyexpanded the clinical role of EEG, with the possible excep-tions of ambulatory monitoring EEG and OR/ICU EEG(Krauss & Webber, 2005). While their premise that an ex-panded clinical role for digital EEG may depend partiallyon validating advanced analysis techniques, e.g. modelingseizure sources, seems reasonable enough, does their im-plicit observation on the clinical worth of ESL still hold,particularly in light of the work carried out in the field inthe last few years? What recent progress has been madetoward the clinical validation of ESL? We explore somefundamental questions that warrant closer scrutiny if ESLis to assume a clinical role in routine epilepsy practice.

    Which part of the spike should be modeled?Lantz and colleagues have carefully examined this issue

    (Lantz et al., 2003b). They wondered how stable the scalpEEG field was from spike onset to spike peak. They basedtheir observations on the spike-averaged recordings of 16patients with symptomatic focal epilepsy. All had an En-gel class 1 surgical outcome. Using a novel spatiotempo-ral cluster analysis technique, they saw, on average, threedifferent voltage field maps during the rising phase of thescalp-recorded spike per patient (range one to five). WhenESL was performed on these different voltage maps, thesource model location coincided with the MRI lesion lo-cation for all patients within a fairly narrow time windowacross the upswing phase of the spikearound the halfwaypoint. Either side of this point, the authors argued that ESLresults were contaminated by noise (toward spike onset)and by propagation effects (toward spike peak). It shouldbe noted that 125 electrodes were used in the study, so theapplication of a half-way point rule to ESL when fewerelectrodes are employed, as is commonly the case, is notentirely clear. Also, because the inverse model used wasa single fit applied to a combined dipolar-distributed al-gorithm, EPIFOCUS (Grave de Peralta Melendez et al.,2001; Lantz et al., 2001) for each field map, spatiotemporalrelationships between successive, independent time-pointfits cannot be fully resolved. Finally, as all spikes were av-eraged, the generalizability of the results to single-spike

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    modeling is uncertain. Still, the study is the first to system-atically quantify the increasingly appreciated concept thatthe surface voltage topography, and by extension the ESLresult, shifts on a scale of tens of milliseconds across theearliest phase of spike interval.

    How well does ESL corroborate epilepsy surgeryfindings?

    The largest prospective study to date on this topic (Boonet al., 2002) looked at the contribution of spatiotemporaldipole modeling to the clinical decision making processin 100 presurgical patients with refractory focal epilepsy.Most cases were lesional (83%), with the largest subgrouphaving unilateral hippocampal sclerosis (53%). Scalp ictalEEG recordings from a 27-electrode set-up (1020 posi-tions plus three inferior temporal electrode pairs) were an-alyzed. From the 93 patients who recorded ictal EEG phe-nomena, 62 patients could not undergo ESL analysis dueto excessive artifact contamination. Of the remaining 31patients, it was concluded that ESL influenced the clinicalinterpretation in 14 cases, usually by confirming the in-congruence between structural abnormality and ictal EEGabnormality (10 patients) and leading to the decision notto proceed with invasive EEG recording and further surgi-cal resection.

    Unfortunately, the study contains several methodolog-ical deficiencies. It was not blinded, it gave no informa-tion on postsurgical outcome, and it performed zero-phaseshift filtering on the EEG raw data. The spatiotemporalmodeling was actually limited to the use of a single (re-gional) dipole, thus making it difficult to disentangle inter-lobar or interhemispheric propagation effects from effectspotentially attributable to multiple independent sources onESL outcome. Also, ESL results were strictly categorizedas type 1 (vertical) and type 2 (radial) dipoles, based on theearlier observations of Ebersole and Wade, who equatedthe type 1 dipole with a mesiobasal temporal lobe source,and the type 2 dipole with a lateral temporal lobe source(Ebersole and Wade, 1990). This classification was subse-quently seen as an oversimplification by Ebersole himself,recognizing the inter-changeability of type 1 and 2 dipo-lar patterns in both forms of TLE (Ebersole, 2000), largelyby virtue of discharge propagation effects occurring earlyin the interictus, and even earlier in the ictus. The clinicalimmediacy of this problem was reemphasized by a recentdepth electrode study that showed that postsurgical suc-cess in medically refractory TLE relies heavily on the spa-tial resolution of the ictal onset zone on a sublobar scale(Chabardes et al., 2005). Lastly, the authors quoted an 8 htime cost for the analysis from start to finish, an experiencethat contradicts recent findings on the relative clinical util-ity of dipole modeling in focal epilepsy (Plummer et al.,2007).

    In the largest prospective interictal ESL study to date(Michel et al., 2004b), a heterogeneous group of 44

    epilepsy surgery candidates undertook a supplementary128 channel surface recording for the purpose of singlesource dipolar-distributed modeling (EPIFOCUS). Of the32 patients who had an identifiable focus, seven of whomunderwent invasive recording in addition to the routinepresurgical work up, all but two patients had concordantESL findings at a lobar level. From a subgroup of 24patients who underwent surgery (17 temporal, seven ex-tratemporal), 18 had an ESL maximum that fell withinthe border of the nearest resection margin (three temporal,three extratemporal were nonconcordant). An Engel class 1outcome was shared by 16 of the 18 cases (mean follow up19 months, range: 733 months). Interestingly, two of thenonconcordant extratemporal cases were mirror localizedto the contralateral hemisphere, their respective presurgicalMRI lesions sitting close to the parieto-occipital midline.Although the intracranial and 128 channel recordings werenot performed simultaneously, the investigators quoted ahigh level of agreement between the intracranially directedinterictal and ictal localization and the high-density surfaceelectrode-directed ESL result (five of seven cases).

    What is especially striking about this study is the degreeof accuracy achieved for the localization despite the factthat each patients MRI brain was morphed to fit a three-shell sphere for the forward model set-up, with standard-ized electrode positions prefitted to the outermost shell.ESL results were constrained to the cortical gray matterand based on the midway point of the averaged spikes up-swing phase. While the results are encouraging, and eachESL analysis was performed in a timely manner, there isthe concern that the investigators were not blinded to thepatients para-clinical data during the source fitting pro-cedure. Comparative results on normal (mock) MRI datawould have further strengthened the case for the robust-ness of their source localization technique. Also, as theinvestigators do point out, resection boundaries are vari-ably wider than lesion boundaries and so measurement biasmay have inflated the accuracy of their ESL results. Ratherthan countering this point by stressing the ESL concor-dance for scalp and intracranial recordings in the few pa-tients who had both performed, a breakdown of the dis-tance from nearest resection boundary to nearest lesionboundary in each of the surgical cases may have been moreinformative.

    The same research group (Sperli et al., 2006) morerecently examined ESL accuracy in a pediatric epilepsysurgical cohort (13 temporal, 17 extratemporal). Interic-tal EEG recordings were acquired using 1929 scalp elec-trodes. A distributed inverse model was applied in this case(depth-weighted minimum norm, MN). The MN algorithmfavors current density solutions that explain surface elec-tromagnetic fields with the least net strength per time point(Hamalainen & Ilmoniemi, 1994). MN solutions thereforetypically localize to the superficial cortex and a mathemat-ical depth weighting term is often applied to counteract

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    this tendency (Michel et al., 2004a). Presumably as a re-sult of the blurred, diffuse nature of the MN-based solution,ESL accuracy was determined in this study by the degreeof overlap (arbitrary 50% minimum) between the resectionboundary (defined as the epileptogenic region) and a sta-tistically deconstructed depth-weighted MN map (earliestvowel-wise activity p < 0.0001 vs. background), whichthe authors defined as the region of discharge onset.The results were encouraging with a 90% concordance be-tween ESL location and nearest resection border and an87% postoperative seizure freedom rate (mean follow up13 months, range: 224 months). The authors argued thatthe three mislocalized cases (all temporal) stemmed fromthe inadequate sampling of the inferior temporal region, apremise that was supported by their corrected modelingof two of these cases when ESL was repeated with a high-density electrode set-up (128 channels).

    As with their previous paper, there was no blinding inthis study. In a majority of the cases (19/30), no postopera-tive MRI was available and the epileptogenic region wasmapped via an interpretation of the surgical notes.

    This study draws out some of the inherent difficulties as-sociated with ESL based on current-generation distributedmodeling methods. With solutions that are as visually se-ductive as functional MRI (Ebersole, 1997a), it should beappreciated that distributed models are based on complexsets of mathematical assumptions that are yet to achieveclinical validation. The use of a statistical discriminatorin the present study might be seen as a step in the rightdirection in this validation process. While the present re-sults need to be reproduced by other investigators, the levelof accuracy obtained with a 1020 electrode set-up doessupport a potential role for ESL in the routine clinicalsetting.

    Finally, it should be stressed that studies using lesionor resection margins to validate ESL results, even in En-gel class 1 cohorts, should be interpreted cautiously. Therelationship between the epileptogenic zone and the puta-tive epileptogenic lesion is far from directfor review seeRosenow & Luders (2001).

    How well does scalp ESL model interictal and ictalonset as defined by intracranial recordings?

    Zumsteg and colleagues also performed statistical post-processing of a distributed algorithm (LORETA) in a retro-spective study of 15 patients with symptomatic mesial TLE(MTLE) (Zumsteg et al., 2005). Unlike the study by Sperliand colleagues (Sperli et al., 2006), they used nonparamet-ric mapping (SNPM), thereby avoiding an assumption thatthe raw ESL results necessarily conformed to a Gaussianstatistical distribution. They compared the interictal local-ization suggested by recordings from foramen ovale (FO)electrodes, which look directly at the hippocampus, withboth the raw and SNPM LORETA results derived from thesimultaneous scalp EEG recording (23 electrode set-up).

    From 19 local field patterns seen by the FO electrodes(11 patterns excluded), 14 could be localized by scalp ESLbased on the rising phase of the spike-averaged waveform.Raw LORETA maps typically showed basolateral tempo-ral activation, while the corresponding SNPM LORETAimages showed more discrete, mesially placed activationthat correlated well with the local FO field. The decisionto exclude 11 patterns from the LORETA analysis due tothe suspected nonfocality of the source (based on the localFO field) seems unusual. One of the benefits of distributedover dipolar modeling is that the former is generally betterequipped to display extended source configurations. Also,while the authors tabulated Engel class surgical outcomesat one year for each patient (most were class 1), they didnot indicate which FO pattern belonged to which patient.

    In a follow-up study (Zumsteg et al., 2006), the in-vestigators used the same patients and the same SNPMLORETA technique to explore the nature of spike propa-gation in MTLE. However, all 30 FO field patterns wereincluded in this study (19 mesial from the earlier study,and 11 lateral). Based on the SNPM LORETA activa-tion sequence, signal propagation was evident in 16 pat-terns, occurred in either direction (mesial to lateral, lateralto mesial), preceded the spike peak, and was not associatedwith Engel class outcome.

    While the authors noted several limitations in the retro-spective study design (reliance on FO electrodes to capturelarge propagating fields, use of a three-shell forward modelcoregistered with a generic brain MRI, and use of only twosupplementary inferior temporal electrode pairs), they re-garded it as the first attempt to examine the accuracy ofa distributed inverse model using simultaneously acquiredscalp and intracranial EEG recordings.

    A different group of investigators (Nayak et al., 2004)used dipole modeling to help characterize the relation-ship between the FO recorded field and the correspondingscalp EEG field in a retrospective study of 20 patients withMTLE. Several important findings came from their metic-ulous analysis of over 4,000 FO spikes. Only 9% of FOspikes were identified de novo at the scalp. Otherwise,either scalp signal averaging (60%) or FO spike correla-tion (13%) was needed to confidently identify the corre-sponding lower voltage scalp spikes (no scalp signal wasseen despite such EEG postprocessing in the remainder,18%). Interestingly, de novo scalp spikes were associatedwith a shallower FO field gradient and were seen up to 2msec later than the small scalp spikes. The latter were as-sociated with a wider scalp field with around 20% of thesepeaking in amplitude at the contralateral scalp. Dipole lo-calization placed de novo spikes at the retro-orbital regionand smaller spikes at the mesial temporal region, but theredipole orientation was more haphazard.

    The authors reasoned that the de novo spikes (100+ mi-croV) were the product of summated propagation from thedeeper mesial source configuration and that the smaller

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    spikes (2040 microV) were the product of volume con-duction. The implication here perhaps is that dedicatedidentification of low-amplitude spikes in MTLE can pro-vide very useful ESL information that is potentially morephysiologically plausible given the fact that dipole mod-eling is fundamentally reliant on the principles of volumeconduction. A criticism of this aspect of the study how-ever, was the use of a single fixed dipole and a single shellmodel. While acknowledging that this simplified model-ing set-up was not designed to interrogate source behaviorat the neurophysiological level, the authors did nonethe-less go on to discuss their ESL findings at this level. Forinstance, it was argued that the retro-orbital dipole local-ization was likely the result of distortion of the scalp EEGfield with the preferential propagation of current throughthe superior orbital fissures. However, it is also quite possi-ble that their use of a modified (Maudsley) 1020 electrodearray (Margerison et al., 1970), which gives electrodes abetter view of inferior brain convexities, effected down-ward displacement of the dipole. When combined with theanticipated opposing effect of spherical forward modelingin MTLE on dipole localization (upward displacement tothe frontal lobe), this might well result in partially com-pensated mislocalization to the retro-orbital region. Forease of comparison, spike peaks were also modeled. If denovo spikes were the product of discharge propagation,then confining ESL to the spike peak may well exagger-ate the influence of signal propagation on the final result.Indeed, as the authors argue, the many discharge patternscaptured at the scalp relative to the deeper FO field werethe probable manifestation of a continuum of source be-havior effectsfrom discharges seen only by the FO elec-trodes, to those seen more globally by virtue of volumeconduction, to those seen slightly later at the scalp by virtueof signal propagation. The probing of such spatiotemporaleffects with a more sophisticated forwardinverse model-ing set-up would have been very worthwhile, particularlyin light of the generally held view that deep mesial dis-charges are only seen at the scalp by virtue of propagationalone (Ebersole, 2003a).

    Finally, the authors did not show patient clinical data (ra-diology, pathology, and surgical outcome), which shouldhave been available, the presurgical work-up having beenperformed between 1990 and 1998. Nevertheless, this workdemonstrates the value of using lower voltage spikes tostudy interictal source behavior in MTLE and it challengesthe view that MTLE scalp spikes are the exclusive byprod-ucts of cortico-cortical propagation from deeper mesialstructures.

    Relatively few ictal studies addressing the correlationbetween scalp and intracranial ESL have ever been pub-lished and, much like the previously described interictalwork, most studies have used presurgical TLE patient co-horts. The main ictal studies are limited to dipole mod-eling and arguably the most influential publication is 10

    years old (Assaf & Ebersole, 1997). The authors exam-ined the EEG recordings of 40 TLE patients who requiredintracranial electrode implantation as part of the surgi-cal work-up. All patients had an Engel class 1 outcomewith a mean follow-up of one year. Spatiotemporal dipolemodeling was carried out on scalp data that had been ac-quired with a 25-electrode set-up (standard 1020 set-upplus three inferior temporal electrode pairs). Dipoles withdifferent orientations were preassigned to model differentsublobar divisions of the temporal cortical surface. Theinvestigators selected the dominant and/or leading dipolemodel that explained the earliest recognizable, averagedictal rhythm seen at the scalp. Dipole models were thenmatched with the ictal localization suggested by the in-tracranial recordings and expressed as positive predictivevalues.

    The results were impressive, with high positive predic-tive values found for the following source and seizureonset match-ups: vertical tangential dipole (basal source)and hippocampal onset (89%), horizontal tangential dipole(temporal tip source) and entorhinal onset (83%), hori-zontal radial dipole (lateral source) and neocortical on-set (80%). Multiple source components were modeled in13 patients in whom an oblique dipole model (geometricmean of above three source components) was thought toexplain seizure onset at the inferolateral temporal cortex.

    As the authors indicated, the ictal ESL results are largelyin agreement with previous interictal ESL results in TLE.This is perhaps not surprising given the relatively good cor-relation between interictal and ictal lateralization in TLE(Blume et al., 2001). It should be noted that, as a retro-spective study, scalp and intracranial EEG recordings werenot simultaneously acquired and the authors did not indi-cate that they were fully blinded to the intracranial datawhen scalp ESL was performed. Also, many seizures werecaptured (212 in total) but the statistical analysis was onlydone on a patient-wise basis. Notwithstanding the Engelclass 1 outcome for all patients, a more rigorous analysisof the consistency of dipole modeling for each seizure ineach patient would have been useful. Moreover, the inves-tigators chose the dipole fit that best explained the earlyictal rhythm but did not describe the adequacy of the best fitin more quantitative terms. For example, it is unclear howwell the dipole model explained the signal variance in eachcase, or how well the leading dipole model dominated thesecond dipole fit when their respective source componentsoverlapped spatiotemporally.

    In a related publication (Assaf & Ebersole, 1999),the authors showed how their dipole modeling approachmight be used to anticipate surgical success following ei-ther standard or modified anteromesial temporal lobec-tomy (AMTL). After a minimum follow-up period of twoyears, they found that postsurgical seizure freedom wasmore likely to occur in patients whose dipole modelingsuggested a dominant or leading basal source, but was

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    most likely to occur in patients whose dipole modelingdid not implicate a lateral source. The latter associationwas explained on the basis that neocortical foci are un-likely to be resected adequately by AMTL, even in themodified approach when the lateral surgical margin isextended.

    Of the few ictal ESL studies that have compared si-multaneously acquired scalp and intracranial EEG in focalepilepsy, it is difficult to look past the methodical study byMerlet and Gotman in which the accuracy of spatiotempo-ral dipole modeling from scalp EEG (2840 channel set-up) was judged in relation to the localization suggestedby depth and/or epidural electrode recordings (Merlet &Gotman, 2001). A consecutive series of 15 presurgical pa-tients with various forms of refractory partial epilepsy wereenrolled in the study. Patients were excluded from ESL ifthey did not have at least two reproducible seizure patternsrecorded at the scalp, one following the other. By specify-ing this condition, the investigators are the first to have sys-tematically examined the spatial resolution of dipole local-ization as the seizure pattern evolves at the scalp electrodes.Of the nine patients (seven lesion-negative) who met thiscondition, six patients (five lesion-negative) had averagedictal patterns that could produce a sufficiently stable andinterpretable ESL result. As noted by other investigators,the earliest ictal rhythm seen at the scalp was always as-sociated with intracranial discharges occupying large areasof cortex. In a new finding though, the three patients withunstable ESL results had, by the time an ictal rhythm wasseen at the scalp, an intracranial ictal rhythm that was bilat-eral with maximal amplitude at the mesial temporal region.Further, of the six patients who returned a stable ESL resultat some point during the seizure, only half had scalp EEGchanges that were concomitant with seizure onset as sug-gested by the intracranial recording. The dominant sourcein each case implicated a neocortical temporal focus withdistances from the main dipole to the nearest (maximalamplitude) intracranial electrode as follows: 5.5 mm, 8.3mm, and 20.8 mm. Two of these patients, and two others(four of six), had dominant dipoles that coincided with thelocations of intracranially recorded voltage maxima duringthe earlier ictal pattern, but this was only the case for twoof the six patients when the second (later) ictal pattern wasmodeled.

    While this study represents one of the most carefullyconducted analyses of ESL to date, the interpretation of thefindings was perhaps slightly misdirected. Much emphasiswas given to the physical separation of the dominant dipoleto dominant electrode in trying to validate the scalp-derived ESL result. The authors did point out that theirquoted distances should not be regarded as error margins,but rather as measures of concordance. However, measuresof spatial inconsistency for ESL results ranged from15.3 mm to 38.4 mm. These distances are modest when

    one considers the following factors: that intracranial EEGlocalization carries an inherent error due to the problemof under-sampling the cortical garland; that dipoles modelcortical surfaces in centimeter dimensions; that dipole ori-entation carries far more weight than dipole location inmodeling source origin and/or propagation; and that theamplitude of the ictal discharge, on which the measure-ments were based, is highly dependent on the orientation ofthe actual source as it faces the recording electrodes. Toillustrate, the authors concluded that mesial onset seizuresare prone to mislocalization by scalp ESL based on theirconcordance measurements from the amygdala (maximalintracranial signal) to the anterior temporal region (dom-inant dipole location) in two patients (20.8 mm and 38.4mm). However, such distances are not unexpected in lightof the recognized patterns of ictal and interictal propaga-tion in MTLE. More useful would have been a clear de-scription of dipole orientation for the spatiotemporal mod-els applied to each ictal pattern for these two patients,but ideally for all patients. Because scalp-derived ESL lo-calization is more likely to reflect discharge propagation,versus discharge onset, assessing ESL spatial accuracy bylocation of the intracranial EEG maxima is problematic.Once again, it is the orientation of the dipoleand the ex-tended area of cortex to which that dipole projectsthatyields a truer indication of ESL validity in modeling ictalor interictal patterns of onset and propagation.

    Lastly, the investigators used a relatively low high-passfilter setting (0.3 Hz) and applied start markers for theirictal modeling at a latency well before any deflection wasnoted in the scalp trace (based on a figure supplied). It istherefore possible that early ictal signal quality may havebeen compromised for ESL, leading to noisier, less-stabledipole solutions.

    It should be emphasized here that the use of intracra-nial EEG recordings as a gold standard validation toolfor ESL is problematic. However meticulously conducted,scalp-intracranial EEG studies, such as those discussedabove, are necessarily limited by the extent to which thecortex is sampled at depth. For instance, FO electrodesonly sample the entorhinal cortex and orthogonal depth ar-rays, as used in the last study (Merlet & Gotman, 2001),have a limited view of the temporal neocortex. Separatingsource origin from propagation will, in such circumstances,involve a measure of speculation on the part of the inves-tigator. The above ESL findings await replication, ideallywith the use of electrode arrays that sample larger areas ofcortex; as with sampling that sufficiently includes mesial,inferior, anterior tip, and lateral temporal lobe cortical sur-faces in the case of TLE interictal and ictal ESL. In fact,without such studies, the capacity for ESL to help charac-terize the interplay between the lesion, irritative zone, theseizure onset zone, and the epileptogenic zone will remainuntested.

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    EEG Source Localization in Focal Epilepsy

    OTHER NONINVASIVE SOURCELOCALIZATION METHODS

    If publication output is anything to go by, MEG andEEG-fMRI have attracted greater research interest than thatenjoyed by ESL in recent times. Signal acquisition tech-niques have improved for both methods, which promisebetter spatial resolution than that offered by current-generation ESL. For MEG, modern high-density sensor ar-rays that encompass the whole head, and not just part ofit, have allowed recordings to be done in a single step,rather than in a cumbersome, piece-wise manner. For EEG-fMRI, novel ways of recording EEG in the hostile MRIenvironment have given researchers the chance to betterunderstand how cortical and subcortical hemodynamic re-sponse patterns are coupled to scalp recorded spike andseizure patterns. For ESL then, there are two immediatequestions.

    Has ESL been marginalized by MEG?It should be emphasized that the principles of signal

    detection and source localization for EEG apply just asequally to MEG. That is, signal detection in MEG de-pends on the recruitment of a sufficient population of dis-charging cortical neurons that are synchronized in timeand aligned in space. And for source localization in MEG,both the forward and inverse problems need to be resolvedwith suitable models to achieve a tenable solution. Thisfactthat MEG-based source localization is answerableto the same kind of fundamental mathematical problemsthat underpin ESLis easily overwhelmed by the glareof the technology on offer. MEG technology is seduc-tive but expensive. Nunez puts it another way. Enthusi-asm for MEG (relative to EEG) has been boosted by bothgenuine scientific considerations and poorly justified com-mercial pressures (Nunez & Srinivasan, 2006b). Even ifMEG becomes more portable and affordable at some fu-ture date, ESL is likely to maintain an important stake innoninvasive source modeling. This is because, apart fromthe obvious practical advantages currently held by EEGover MEG (pediatric and ictal studies, long-term moni-toring are largely prohibited by head movement artifacts),EEG and MEG see spike and seizure discharges quitedifferently.

    The practice of pitting MEG against EEG in the sourceimaging literature has perhaps been overdone, and thereis now an evolving consensus that the combined use ofthese techniques (in the rare situation when both are ac-cessible) optimizes source localization accuracy (Fuchs etal., 1998; Barkley & Baumgartner, 2003). While compar-isons between the two methods will continue to be made,it should be noted that to date no study has been pub-lished which compares source modeling accuracy for si-multaneously acquired MEG and EEG recordings againstsimultaneously acquired intracranial data (as the surrogate

    gold standard) in a prospective, blinded manner for focalepilepsy.

    A common misconception in this regard is that MEG ismore accurate than EEG in defining source activity ow-ing to its superior spatial resolution and its relative immu-nity to field distortion by volume conductor effects (Nunez& Srinivasan, 2006b). While these latter qualities are trueenough, MEG only picks up part of the cortical activitygenerated by the source(s). To coin an analogy, one mightimagine that if a patch of discharging cortex is likened toa 10-cm2 piece of undulating cheese, EEGs field of viewwill be blurred and distorted by the overlying cellophanewrapper, while MEGs view will be clearer, but restricted,as if the cheese had been made Swiss. This is becauseMEG is blind to the radial vector component of the elec-tric field. Thus, the magnetic field is less complicatedby variably admixed radial and tangential vectors and it isless distorted by the skullscalp interface. However, MEG-based modeling has an inherent bias for superficial sourcesbecause the magnetic field decays very rapidly from scalpsurface (Hamalainen et al., 1993).

    It is interesting to note that MEG source imaging (MSI)researchers have almost exclusively applied single fixeddipoles to model spikes in focal epilepsy. While the use ofsuch a simple inverse algorithm in MSI might better suit themodeling of the cleaner signal topography of magneticversus electrical fields, it is a mistake to think that MEG isimmune from the same properties of signal propagation asEEG. Gloor puts it bluntly. Modeling the magnetic fieldbased on the assumption that the field can be representedby a single fixed dipole is fraught with difficulties similarto those inherent in the modeling of electrical fields basedon this assumption (Gloor, 1985).

    Therefore, the interpretation of MEG source localizationaccuracy on the millimeter scale, as is the usual case in theMSI literature, should be eyed with at least a degree of cau-tion. Indeed, the area of cortex required for contemporaryMEG arrays to detect an interpretable field is still in the or-der of 3 cm2 at best (Oishi et al., 2002). A recent paper byFischer and colleagues has looked to redress this issue withthe calculation of ellipsoid volumes based on dipole clustervariability for a population of spikes in a presurgical group(Fischer et al., 2005).

    It is repeatedly emphasized in the MSI literature that,although blind to radial source components (from gyralcrests), most of the cortex is seen by MEG arrays because,as observed by Brodmann nearly 100 years ago, the cumu-lative gyral surface only accounts for a third of the totalsurface area of the human brain (Brodmann, 1909). Butthe implication here is that anatomy and physiology aremeasured with the same ruler. As Wong reminds us, cor-tical lamination, pyramidal arborization, cortical vascular-ization, and cortico-cortical connections are richer at gyralcrowns than at fissural walls (Wong, 1998). Gyri also ac-count for much of the cortical homunculus in man (Welker,

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    Figure 4.Demonstration of the relationships between cortical anatomy, surface electrical field, and dipole model for a rightfrontotemporal spike in a patient with focal epilepsy. T1-weighted MRI coronal (A) and sagittal (B) views; and surfacerendered cortical images, oblique (C) and from above (D), are shown. Note that dipoles (rotating over 40 msecepoch) and the associated confidence ellipsoid volumes do not conform to either the position or orientation of aparticular sulcus, gyrus, or even lobe (A, B). Rather, dipoles model the summated electrical field recorded at thescalp electrodes (surface negative over right hemisphere and surface positive over left hemisphere in this case, C),and they assume orientations and positions that attempt to explain the polarity characteristics of this surface field(D). Therefore, large areas of cortex, comprising multiple sulci and gyri, are represented electrically in the modelingof interictal or ictal events. It is the net configuration (orientation, position, and strength) of micro dipolar fields(variably seen by the surface electrodes as they project orthogonally from numerous, individual gyral and sulcalsurfaces) that is ultimately modeled by the dipole solution. Abbreviations: MRI (Magnetic Resonance Image).Epilepsia C ILAE

    1990; Wong, 1998). Much of the immediate fissural regionis dedicated to propagation of interictal/ictal discharges,a phenomenon that is generally ill suited to single fixeddipole modeling strategies as discussed earlier. This is es-pecially so if modeling is restricted to the spike peak, apractice commonly adhered to in MEG epilepsy studies.The redundancy of the anatomical two-thirds argumentis made clearer when it is recalled that dipoles are notanatomically based constructs, but representations of the

    summated electrical field generated by areas of cortex largeenough to include both gyral and fissural surfaces (Fig. 4).

    Despite these caveats, support for MSI as a legitimatesource localization technique has grown. Many clinicalstudies, most of which are understandably interictal, havefound favorable correlations between MSI location andthe localization suggested by either intracranial record-ings or postsurgical seizure recurrence ratesfor keynotestudies see (Stefan et al., 2003; Pataraia et al., 2004) and

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    EEG Source Localization in Focal Epilepsy

    for reviews see (Ebersole, 1997b; Barkley, 2004). How-ever, as pointed out in a more recent review (Makela etal., 2006), no large prospective trial incorporating a ran-domized control group (with sham lesion margins) hasbeen carried out. Most trials have been performed retro-spectively on small patient groups when investigators havenot been blinded to the patients corresponding MRI data.Similarly, levels of intra- and interoperator concordancefor MSI localization results in these studies have not beenclearly assessed.

    Can EEG-fMRI optimize the spatial resolutionof ESL?

    The ability to correlate the interictal waveform of EEGwith the blood oxygen level dependent (BOLD) responseof fMRI is a relatively recent development in functionalimaging in epilepsy (Seeck et al., 1998; Goldman et al.,2000; Krakow et al., 2000). Several authors have proposedthat simultaneously acquired fMRI should enhance the spa-tial resolution of ESL (Liu et al., 1998; Krakow et al., 1999;Phillips et al., 2002). The idea is that spike-correlated fMRIdata can be used to constrain the ESL solution to one orother regions of interest. Such a constraint might be ex-pected to hold more physiological credence than traditionaldipole or distributed ESL constraints that are founded onrelatively pure mathematics.

    However, the EEG-fMRI marriage has not been so cosy.Accumulating evidence suggests that the spatiotemporalrelationship between the two is far from straightforward.To begin with, the temporal resolution of fMRI, which isdependent on the properties of blood flow and blood de-oxygenation, lags EEG by a factor of 1,000. This meansthat the electrical field and the hemodynamic response gen-erated by an epileptiform discharge must be coupled byan archetypal time-constant, the so-called hemodynamicresponse function (HRF). Aside from the variability inthe HRF that can occur between individuals (Aguirre etal., 1998) and between spike populations (Bagshaw et al.,2004), the validity of a one-size-fits-all HRF must be ques-tioned when the pathology presumed to be responsible forthe patients epilepsy interferes with the regional integrityof the normal bloodbrain barrier (viz. gliosis, oedema,vascular malformations). And, despite the promising spa-tial resolution of fMRI, (which, it must be remembered, isheavily influenced by operator-dependent thresholding ofthe signal), the relative temporal blurring of the BOLD re-sponse makes it virtually impossible to tease out dischargeonset from discharge propagation in contemporary EEG-fMRI recordings.

    The disjointed spatiotemporal relationship between EEGand fMRI is underscored by the fact that the fMRI BOLDsignal is a physiological response tied to brain structure andone that is not dependent on the synchronized and alignedneuronal activity on which both EEG and MEG are based.Because scalp EEG is used to signpost interictal and ic-

    tal events for interpretation of the BOLD, one is left withanalyzing only that fraction of the BOLD activity that hasbeen lassoed, however loosely via the HRF, to electricalevents visible at the scalp. Electrical events unseen at thescalpand estimates of simultaneously detectable surface:depth spikes have been put as low as 1:2000(Alarcon etal., 1994) are, by default, forfeited when it comes to thefMRI BOLD analysis. The converse approach, that is, us-ing ESL to understand the significance of BOLD activ-ity in focal epilepsy, seems more logical. A recent EEG-fMRI study in benign partial epilepsy with centrotemporalspikes suggests that multiple spatiotemporal dipole model-ing can help unravel the temporal sequence of BOLD ac-tivation associated with interictal discharges (Boor et al.,2007). In this study, centrotemporal spikes were character-ized by an initial dipole corresponding to BOLD activationat the central sulcus; and a second, later dipole correspond-ing to BOLD activation at the sylvian fissure. Based on theESL sequence, the pattern of BOLD activation was thoughtto capture the spatial onset and propagation of the corre-sponding electrical event.

    Still adding to the complexity of the EEG-fMRI rela-tionship is the observation that both neuronal excitationand inhibition can lead to an increase in the BOLD sig-nal (Gotman et al., 2006). This is because the BOLDsignal is thought to reflect neuronal energy consumption ina broad sense. Less intuitive then, is the significance of adecrease in the BOLD signal, or deactivation. It has beenproposed (Kobayashi et al., 2006) that deactivation mightarise from a generalized state of functional de-afferentation(akin to a state of suspended animation); or, from a rela-tive increase in inhibition occurring at the level of the soma,a process that is less energy consuming than inhibition oc-curring at the level of the postsynaptic membrane.

    In short then, given our incomplete grasp of the funda-mental determinants of the BOLD response in EEG-fMRIsignal acquisition, and given the relatively inharmoniousrelationship between EEG and fMRI signal dynamics, theuse of fMRI as a default ESL constraint cannot be recom-mended at this stage. Much like MSI, more can be gainedby respecting the independence of the two methods forsource localization in focal epilepsy. Indeed, the converse,the use of ESL with EEG-fMRI to rationalize the temporaldynamics of fMRI, may be a more fruitful approach.

    CONCLUSIONThis review has examined the current state of EEG

    source localization (ESL) as it applies to focal epilepsy.We have outlined the important principles that guide themethodological approach to contemporary ESL and wehave highlighted some of the common misconceptions re-lated to dipole and distributed modeling.

    Based on the progress made in clinically directed re-search in the last 510 years, we believe that ESL will

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    earn a place in the routine work-up of patients with fo-cal epilepsy in the foreseeable future. To get there, we an-ticipate that the following conditions need to be met: (a)blinded, prospective validation studies conducted on largerclinical groups; (b) the more routine use of realistic for-ward models; (c) a greater awareness of the importance ofsource orientation in defining inverse modeling solutions,with elimination of the tendency to use source location asthe cardinal determinant of ESL accuracy; (d) the avoid-ance of restricting source modeling to spike peaks such thatearlier spike components are routinely included in the ESLanalysis; (e) the avoidance of using zero-phase shift filter-ing on EEG signals; (f) the inclusion of error measures,such as confidence ellipsoid volumes, into ESL solutions toimprove the statistical robustness of clinically based find-ings; and (g) when permitted by the SNR, clarification ofthe likely clinical impact of choosing single versus aver-aged spike epochs for ESL.

    We have argued that ESL has by no means been sweptaside by the more advanced functional imaging modali-ties of MEG and EEG-fMRI, principally because the threemodalities emphasize quite different bio-electromagneticphenomena. As with all investigative modalities used inthe clinical work-up of patients with focal epilepsy, in-cluding intracranial EEG localization, each is beset by itsown problems in trying to define the epileptogenic zone.This is perhaps a better way to judge the relative meritsof source localization strategiesby understanding the re-spective limitations of each method from the start.

    With this approach, loose assumptions and dogmaticconclusions are less likely to be made. In fact, the lead-ing source localization approach, or the one that shouldbe most valued, has not changed much in nearly a cen-tury despite the relatively recent boom in anatomical andfunctional imaging in epilepsythe scalp recorded EEG inthe context of a rigorous patient history. The multimodalimaging technology on offer today for spike and seizurelocalization has limited value if its results are interpretedwithout due respect for the patients electroclinical seizuremanifestations. In this sense, ESL is better regarded as amuch underutilized three-dimensional extension of tradi-tional two-dimensional EEG analysis. As a technology thatis now quite accessible, affordable, noninvasive, and onethat is founded on the well-established electrophysiologi-cal principles of EEG, ESL continues to hold promise asa potential clinical tool that offers to teach us much aboutthe recruitment and propagation of interictal and ictal dis-charges in focal epilepsy.

    ACKNOWLEDGEMENTSThe authors would like to thank Dr. Michael Wagner and Dr.

    Manfred Fuchs for their patience and support in explaining (andreexplaining!) the often-vexed mathematics that goes hand-in-hand with EEG and MEG source localization; and Mr. SimonVogrin, Mr. Lucas Litewka, and Dr Kevin Morris for their en-

    couragement and debate on the topic. The first author would liketo personally thank Dr. John Ebersole for his many lively presen-tations on dipole modeling. Perhaps more than anyone else in thearea, he has strived to keep the discipline grounded in its clinicalroots while always endeavoring to keep the mathematicians andphysicists honest!

    The first author holds an Australian National Health and Med-ical Research Council Postgraduate Medical Research Scholar-ship. We confirm that we have read the Journals position on is-sues involved in ethical publication and we affirm that this reportis consistent with those guidelines. There are no disclosures fromany of the authors.

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