EEG source localization in focal epilepsy: Where are we now?

Transcription

1 CRITICAL REVIEW 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 interest in clinical epileptology. This has been driven by innovations in the computer-assisted modeling of dipolar and distributed sources for the investigation of focal epilepsy; a process fueled by the everincreasing computational power available to researchers for the analysis of scalp EEG recordings. However, demonstration of the validity and clinical 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 examining 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 routine epilepsy practice, underlining some of the current 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, Distributed, Electroencephalography, Magnetoencephalography, Functional magnetic resonance imaging. CONCEPTS AND CONTROVERSIES In the last five years, research in the field of electroencephalographic source localization (ESL) produced more than 150 scientific papers on computer-assisted mathematical techniques for dipolar and distributed source modeling. By comparison, less than half of this number of publications addressed the clinical validation of such techniques for the investigation of focal epilepsy. Most clinical studies featured less than 20 subjects and few were conducted prospectively. Such an imbalance might be explained 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 October 17, Address correspondence to Chris Plummer, Centre for Clinical Neurosciences and Neurological Research, St Vincent s Hospital, 5th Floor Daly Wing, 35 Victoria Parade, Fitzroy, Victoria, Australia chris.plummer@svhm.org.au Blackwell Publishing, Inc. C 2008 International League Against Epilepsy to the clinical validity and utility of these mathematically complex, often nonintuitive, modeling techniques. As such, will ESL ever realize a place in the routine workup of patients with focal epilepsy? If so, what is the current 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 combined EEG-functional magnetic resonance imaging (EEGfMRI)? The fundamentals of ESL As a discipline that aims to localize the sources of electric 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 electroencephalographers; that is the noninvasive localization 201

2 202 C. Plummer et al. Figure 1. (A) Scalp recorded 19-channel EEG using Common Average Reference from a patient with BFEC. Values at the extreme right of channel waveforms correspond to surface potential points (in microvolts) at the msec latency marker (midway between onset and offset interval markers, asterisked). As is customary, negative values indicate 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, larger surface negative temporal sharp wave (at offset marker, maximal at T5). (B) Butterfly plot showing superimposed waveforms for the 19 channels. (C) Isopotential field plots for 36-msec period following peak of early spike wave component of same BFEC discharge. Note the polarity inversion of the dipolar field that occurs across the course of the discharge. The left frontal field is initially surface negative (blue) and later surface positive (red), while the left temporal field is initially surface positive (red) and later surface negative (blue). The isopotential lines demonstrate that the later surface negative temporal field has a broader distribution than the earlier, more concentric, negative frontal field. Abbreviations: BFEC (Benign Focal Epilepsy of Childhood), MGFP (Mean Global Field Power). Epilepsia C ILAE of epileptogenic networks in the patient presenting with epilepsy. Two fundamental problems exist in the practice of ESL forward and inverse. The forward problem is solved by specifying a set of conditions (compartments, surfaces, conductivities) for the head model, also referred to as the volume conductor or forward model. The forward model by analogy is the stage on which the source, or source network, performs, its projected (lead) field passing through modeled compartments and tissue interfaces to reach the recording electrodes. It is the set of conditions specified to the forward problem that distinguishes one forward model from another. Forward models range from simple (a single spherical shell models the brain surface) to complex (a four-layered realistic model, its compartments segmented from the patient s MRI scan, models the brain, cerebrospinal fluid, skull, and scalp surfaces). Spherical shell and realistic models are the two versions of forward modeling used in ESL today (Fig. 2). The former vary in complexity from single shell to multiple (two, three, or four) overlapping shell models, and the latter are sub-divided into boundary and finite element method (BEM, FEM) models. We touch on the application of these models in the next section. For a specific electrical source, the forward model will enable the computation of a specific potential field at its surface (Wilson &

3 203 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 overlapping shell 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 approximate the shape of the human brain than do shell models. This is particularly the case at the brain s 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), each layer having been computer generated from segmented T 1 -weighted MRI surfaces (scalp, skull, CSF, and cortex). Different compartments are usually given different conductivity values, but conductivity within each compartment is assumed 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 tissue anisotropy can be factored into algorithms that solve the forward problem. As distinct from the 4-shell model, note how 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 Resonance Image). Epilepsia C ILAE Bayley, 1950). Thus, the forward problem will give a unique solution. The inverse problem, by contrast, has no unique solution. That is, an infinite number of source permutations can, in theory, explain a specific potential field recorded at the surface (Helmholtz, 1853). This is the problem the electroencephalographer attempts to solve in routine clinical practice, traditionally with a mind s eye rendering of candidate sources drawn from the various EEG montage digital displays. In practice, the experienced electroencephalographer constrains the infinite possible solutions to the inverse problem by applying their working knowledge of epileptogenesis in the focal epilepsy syndromes to the patient s clinical picture, supplemented perhaps by information from anatomical or functional imaging studies. Similarly in ESL, the inverse problem is made soluble by the incorporation of mathematical constraints into inverse modeling algorithms. Just as volume compartment and boundary conditions distinguish one forward model from the next, constraint conditions distinguish one inverse model from the next. The two major inverse modeling approaches are the dipolar and distributed modeling methods. The mathematics of inverse modeling can be quite complex and it is not the purpose of this paper to summarize the algebraic pros and cons of the several dipolar and

4 204 C. Plummer et al. Figure 3. (A) ESL example of a Moving dipole inverse model used with an FEM forward model based on the same BFEC sharp wave complex depicted in Figure 1. Each dipole is fitted to a single time point at successive 4-msec intervals across that part of the interictal discharge shown in Figures 1A, 1B. Relative dipole size is in proportion to dipole strength or amplitude. Note that each color-coded dipole symbol is made up of a spherical end directed toward the surface negative cortex and a straightened tip directed toward the surface positive cortex. Hence, the earlier, smaller surface negative frontal sharp wave seen in the BFEC discharge is represented by the smaller, darker green dipoles, while the larger, 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). It is worth pointing out that different types of dipole symbols are used in the ESL literature, but investigators do not routinely spell out surface negative and surface positive aspects of such dipole symbols. (B) LORETA distributed inverse model used with a BEM forward model. Note the relatively diffuse distribution of the current density map spanning the left central sulcal region. Broad, blurred ESL solutions are quite typical for the LORETA method. Brighter signal intensities indicate higher current density values (scaled as microamperes per square millimeter). Orthogonal axes are shown (+x left, +y posterior, +z upward). Appreciate that the single time point chosen for display ( msec) approximates the halfway point of the upswing phase of the surface negative discharge, a point which, on current evidence, 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 ESL for reviews see (Darvas et al., 2004; Michel et al., 2004a). What should be stressed though is that dipolar methods are overdetermined (Fuchs et al., 1999) in the sense that the investigator preselects one, two, or three (rarely more) dipoles to apply to the inverse algorithm in question. This means there are far more data sampling points (viz. electrodes) than there are dipole parameters in determining the ESL solution. Two of the most commonly used inverse algorithms in ESL are the moving and rotating dipole methods. The moving algorithm constrains the dipole to instants in time (successive 4 msec time instants in a 256 Hz recording are usually solved); but frees it in space to assume a location, orientation, and strength to best explain the measured EEG data at each time instant (Fig. 3A). The rotating algorithm constrains the dipole to a location in space; but frees it to assume 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 sampling points than possible ESL solutions. This is because, unlike dipole modeling strategies, no assumption is made on the number of dipoles used to solve the inverse problem. Instead, the working premise is that multiple sources may be simultaneously active across multiple locations at a given instant in time. The predefined solution space (be it the whole brain volume or just the cortical volume) is split into multiple points, each point representing a minidipole, fixed in space but free to assume any orientation and strength. Due to the enormous number of permutations that stem from such mini-dipole networks, all offering a theoretically plausible explanation for the measured EEG signal, postprocessing constraints need to be applied to achieve a unique ESL solution.

5 205 EEG Source Localization in Focal Epilepsy Low-resolution electromagnetic tomography (LORETA) is one of the more familiar distributed modeling algorithms used in ESL (Fig. 3B). LORETA applies a modeling constraint based on the idea that neighboring neuronal populations are more likely (than nonneighboring ones) to undergo synchronous depolarization during a spontaneous discharge or an evoked response (Pascual-Marqui et al., 1994). LORETA modeling tends to generate broad, smoothed ESL solutions as neighborhood sources are model term conditioned to assume similar strengths. As discussed later, such assumptions may not always be in touch with electrophysiological reality. It is often misunderstood that the inverse and forward problems are interdependent. They are only interdependent in the sense that both are required to generate an ESL solution. However, the mathematical algorithms specified to each problem are independently set (Scherg et al., 1999). This is important to appreciate because any ESL solution can be reached when any inverse model is coupled with any forward model. The two major challenges then, lie in (a) deciding on which of the available forward and inverse models are the most appropriate to apply, and (b) translating the theoretical impact governed by the choice of a particular forward inverse modeling set-up into terms that define the clinical impact of such a choice on the patient s diagnosis and management. Scant attention has been given to the latter in the ESL literature to date. Most clinical studies preselect a particular forward inverse modeling combination (shell or realistic-dipolar or distributed) and test its performance in a particular clinical setting either directly, using simultaneously acquired intracranial EEG, or more commonly indirectly, using estimates of concordance with other functional and anatomical studies. On the forward problem and its problems The relative disconnect at the technical clinical interface of ESL research is exemplified by the forward problem. The digital reconstruction of realistic forward models that predict the impact of volume conduction on the generation of scalp potentials remains a central theme in biophysics EEG research. But on this point, Niedermeyer reminds us of Jasper s observation 40 years ago that propagation along conducting pathways represents the most important mechanism of signal spread (especially as regards epileptiform signals) (Jasper, 1969), himself warning that excessive emphasis on volume conduction and total reliance on biophysics are not the answer to EEG signal analysis (Niedermeyer, 2005). There is little doubt that the modeling of basal source activity, as in temporal lobe epilepsy, is optimized with the use of realistic head models that more accurately delineate the nonspherical, inferior aspects of the brain compared to overlapping shell models (Ebersole, 1997a). The latter give dipole location errors of up to 30 mm in the rostral direction spherical shell models commonly mislocalize known mesial temporal lobe source activity to the frontal lobe (Cuffin, 1996; Roth et al., 1997). Errors in dipole orientation were also noted to increase when spherical models were used in place of realistic models in a more recent simulation study (Crouzeix et al., 1999). While these observations are important, it should be appreciated that the effects of signal propagation (primarily via cortico-cortical pathways) on scalp EEG voltage topography are not factored into equations designed to solve the forward problem. It is probably under-emphasized that in terms of presentday clinical utility, the property that differentiates spherical from realistic head models simply relates to model shape, rather than to any more advanced feature (Fuchs et al., 2002). Spherical models conform to frontal and parietal brain convexities reasonably well but are found wanting when it comes to the modeling of infero-occipital, infero- and mesial temporal, and orbitofrontal brain surfaces (Ebersole, 2003a), regions that commonly play host to the epileptogenic zone in focal epilepsy. Research efforts aimed at further advancing realistic models, such that they are brought closer to reality, remain hamstrung by three factors. The first is the extra computational demand that complicates the integration of tissue anisotropy parameters into the forward modeling algorithm. While finite element methods aim to satisfy the more physiological anisotropic, heterogeneous conduction that takes place within tissue compartments and across tissue boundaries (Fuchs et al., 2007); in practice, most realistic models (viz. boundary element methods) assume homogenous, isotropic conduction within compartments and limit conductivity variability to surface boundaries alone. The second factor is the absence of agreed-upon tissue conductivity values for the various tissue compartments in the human head. For example, the wide range of values published for the skull-to-brain conductivity ratio in humans is at least partly due to inherent discrepancies between in vitro and in vivo based findings (Nunez & Srinivasan, 2006a). Clinical studies often apply conductivity values for the brain, skull, and scalp that are based on an in vitro study published 40 years ago (Geddes & Baker, 1967). In any case, a more recent in vivo study suggests that the inter-individual variability of these properties may limit the generalizability of tissue conductivity values for realistic models (Ha et al., 2003). Moreover, while it has been theorized that the brain skull interface dampens the voltage of a dipole point to one-eighth of its strength, it should be kept in mind that skull conductivity actually improves with increasing skull thickness due to the accompanying increase in the ratio of cancellous to cortical bone marrow conductivity being higher than that of cortical bone (Nunez & Srinivasan, 2006a). Thus, the individualization of forward modeling would expectedly depend on regional nuances in individual skull thickness and cranial contouring relative to the brain, the latter influencing

6 206 C. Plummer et al. electrode distance and electrode orientation relative to the cortical surface (Binnie et al., 1982). The third problem that needs to be resolved is the clinical relevance of incorporating brain tissue anisotropy into forward modeling calculations via finite element methods. Average white matter resistivity is approximately double that of gray matter, largely by virtue of the multidirectional nature of white matter fiber tracts (Nunez & Srinivasan, 2006a). While diffusion tensor imaging (DTI) techniques are beginning to avail quantification of white matter anisotropy (Mori & van Zijl, 2002), the relative clinical impact of such a parameter on volume conduction (vs brain-skull and scalp-air interface effects) remains unqualified. Although speculative, anisotropic modeling of brain tissue may offer some much needed insight into the biophysical properties of interictal and ictal source propagation occurring across cortical regions. The immediate relevance of this becomes apparent when one considers that approximately 98% of pyramidal cell input in humans is via cortico-cortical connections (Braitenberg, 1978). On the inverse problem and its misconceptions Just as forward models vary in complexity from single shell models to multicompartment realistic models, inverse dipolar models range from single fixed dipoles fitted to single time points (as with fixed and moving dipoles) to multiple dipoles that overlap in three-dimensional space across time intervals (as with rotating and regional spatiotemporal dipoles). Likewise, distributed models can be broadly classified into those that use linear mathematics (as with the so called minimum norm, depth-weighted minimum norm, and LORETA models) and those that use nonlinear mathematics (as with L1 norm methods). However, rather than presenting a didactic discussion on the variety of inverse algorithms, both dipolar and distributed, available in ESL today (some of which we discuss further in the next section), we will address some of the key concepts in inverse modeling as a series of common misconceptions. (A) Dipole models provide point-like anatomical solutions for spike and seizure localization It is tempting to interpret dipoles as point sources of interictal or ictal activity when they are pitched, as is often done in publications, over a coregistered image from the patient s MRI scan. This is especially so when ESL studies not uncommonly infer that a dipole s x, y, z location in space is the sine qua non of a dipole model s accuracy. This misconception is understandable given the millimeter margins of error that are often cited for dipole positions in simulation studies, particularly in the biophysics literature. As Ebersole has repeatedly emphasized, a dipole is not a discrete anatomical construct but a theoretical concept on which the modeling of relatively large segments of synchronously discharging cortex is based (Ebersole & Hawes-Ebersole, 2007). The threshold cortical area for a spike to be seen by the scalp electrodes is 10 cm 2 (Tao et al., 2005). The orientation of the dipole is just as, if not more, informative of the behavior of a putative source. The state of a dipole s orientation within milliseconds of spike or seizure onset can shed light on patterns of corticocortical propagation, a phenomenon not easily appreciated from the traditional visual inspection of the EEG waveform alone (Ebersole, 1994). (B) The dipole marks the center of mass of the source This is an oft-quoted phrase in the ESL literature. Unfortunately, as a definition it is somewhat vague and, as Gotman points out, there is actually no proof for it despite its longevity as a concept (Gotman, 2003). Rather than visualizing a dipole as the center of a mass of equivalent current, simultaneous surface-depth recordings suggest that, for a given time point or time interval, the dipole most reliably models the maximum potential of a source whose intracerebral field is often quite extensive (Merlet & Gotman, 1999) and whose strength, location, and orientation can change quite rapidly across the time course of an epileptiform discharge. (C) As artificial concepts, dipole and distributed models carry little electrophysiological relevance Electroencephalograph literally means electrical brain picture electro-encephalo-gramma being the Greek roots (Knott, 1985). Our present understanding of EEG signal generation is actually based on the electrophysiological theory that the EEG waveform is the product of a myriad of dipoles, or dipolar configurations, that flux in polarity within the cortical space (Brazier, 1949; Gloor, 1985; Ebersole, 2003b). The electromotive force behind dipolar field generation is the resting membrane potential of the pyramidal neuron. Following its excitation at the postsynaptic membrane, the pyramidal cell experiences a progressive wave of depolarization along the length of the axon. Passive loops of extracellular current are set up to complete the local circuit (Buzsaki et al., 2003). It is the linear summation of the extracellular components of these pyramidal cell microcircuits, positive and negative, that configures the source and its projection to the scalp as a recordable potential field. It is useful to visualize this extended source activity through the conceptual lens of Gloor s solid angle theory (Gloor, 1985). Perhaps the most common error in EEG reading is to see the electrode that registers the peak voltage of an epileptiform discharge as the one that lies closest to the source (Ebersole, 2000). Gloor s theory reminds us that it is the cortical configuration (area and orientation) of the source in relation to the recording electrode, rather than the source-to-electrode distance, that determines EEG surface polarity. With this is mind, it can be appreciated that both surface polarity maxima (positive and negative) of the potential field carry useful localizing information (Fig. 1C). For instance, it is typically the contralateral surface-positive, and not the ipsilateral surface-negative, potential field maximum that better delineates the origin

7 207 EEG Source Localization in Focal Epilepsy and propagation of ictal or interictal source activity in mesial versus lateral temporal lobe epilepsy (Ebersole & Wade, 1990). In electrophysiological terms, it is important to keep the spatial limitations of dipole modeling in perspective. One cubic millimeter of human neocortex contains around 10 5 neurons and 10 9 synapses. One scalp electrode is estimated to record the synchronized and aligned space-averaged potentials of around 10 8 to 10 9 neurons (Nunez & Srinivasan, 2006a). One dipole models upwards of around 10 cm 2 of cortical tissue. This is why it is important not to regard dipole solutions as point-like millimeter (let alone submillimeter) indices of abnormally discharging cortex. By the same token, such a logarithmic leap in spatial dimension has led Nunez to propose that, should there be a major shift in our future understanding of epileptogenesis at the micro-scalar level (cellular and subcellular), the electrophysiological relevance of dipole modeling theory at the macro-scalar level (lobar and sublobar) will likely remain intact (Nunez & Srinivasan, 2006a). It is rather the next tier the application of macro-scalar theory, in the form of ESL, to routine epilepsy patient work-up that needs more rigorous proof of concept at this point in time. (D) The surface negative peak of the highest amplitude spike from the scalp EEG recording gives the most reliable ESL result This is both partly true and false. It is technically true because dipole and distributed modeling solutions are most stable when the signal to noise ratio (SNR) is highest, as is usually the case at the spike peak. The stability of an ESL solution, or more specifically, the parameters that define it (location, orientation, strength), relates to its reproducibility and, pending the suitability of the forward inverse modeling set-up, to its capacity to explain the signal variance at the scalp electrodes. Along these lines, a recently introduced strategy to help quantify the probability of an ESL solution is the confidence ellipsoid (CE) volume calculation (Fuchs et al., 2004b). Dipoles fitted with CE volumes are, by definition, free to roam within the confines of the ellipsoid space without its inverse fit parameters impacting on the forward fit solution beyond the level of noise attached to the solution subspace (Fig.3A). In other words, the smaller the CE volume, the greater is the probability that the dipole resides at the fit location for a given time point or time interval. An inverse relationship between the CE volume and the SNR has been demonstrated in both simulation (Fuchs et al., 2004b) and clinical (Plummer et al., 2007) studies. Thus, small CE volumes tend to occur in the vicinity of the spike s peak where the SNR is typically higher. The above tenet is, however, misleading in terms of the probability that the ESL result actually models the original interictal or ictal source. It has become increasingly recognized that ESL results based on spike peak activity, an approach that is still seen in clinical studies, should be interpreted with caution. This is because simultaneous surfacedepth recordings reveal, perhaps not surprisingly, that it is the earlier component of the epileptiform discharge at the scalp, which most closely matches the location and field of the source as suggested by the corresponding intracranial EEG activity (Fig. 1A, 1B). At the scalp recorded spike peak, the signal is often well removed from the original source due to the effects of cortico-cortical propagation. The problem then lies with the accurate modeling of earlier phase interictal or ictal activity when the signal is often buried in noise. Scherg has emphasized that source activity onset is best demarcated with a higher low-filter setting, recommending a 2 10 Hz frequency threshold range instead of the more traditional Hz cut-off (Scherg et al., 1999). The effect is to minimize the contribution of slower frequencies that are less likely to figure in the earliest source activity. In a similar vein, he stresses the importance of using a forward noise filter for ESL, rather than a zero-phase shift filter, as the latter tends to artificially blur signal onset and offset. Assuming technically satisfactory EEG signal acquisition, the SNR is commonly optimized by averaging single events. However, averaging carries the inherent risk of mislocalizing single events if the latter are not truly monomorphic (Braga et al., 2002; Chitoku et al., 2003). Various methods, such as phase coherence and global field power correlation (Lehmann, 1987), have been used to help pool identical discharges for averaging purposes. While averaging can improve the localizability of the earlier components of focal epileptiform discharges (to the point where it tends to be done routinely in research), few studies have rigorously examined the clinical impact of single versus averaged event selection in dipolar and distributed modeling. (E) ESL is too cumbersome to perform. Too many electrodes, too much computer knowledge, too many difficulties with image coregistration, and too many time demands make it impractical for routine use in the clinical setting There remains no fixed agreement on the minimum number of scalp electrodes required for clinically useful ESL in focal epilepsy. While high-density electrode arrays can improve the spatial resolution of surface EEG signal topography, and thus facilitate the task of distinguishing source origin from source propagation, there is the penalty of having to measure and fix hundreds of electrodes to the scalp. On theoretical estimates, the minimal number of scalp electrodes required for optimal EEG spatial resolution, the so-called Nyquist criterion, lies between 100 and 200 (Gevins, 1993; Srinivasan et al., 1996). Electrode caps are not an ideal answer to this problem as electrode-scalp contacts can be unreliable and their use for long-term EEG monitoring is impractical. The loading

8 208 C. Plummer et al. of scalp electrodes over putative cortical foci, as with the use of inferior temporal arrays in temporal lobe epilepsy, can provide clinically valid ESL results based on intracranial EEG localization (Ebersole, 2003a). Dipole simulation work has also demonstrated that nonuniform sampling with scalp positions in the region of interest and positions elsewhere can provide reliable estimates of source characteristics (Benar & Gotman, 2001). A more recent study in a group of 14 patients with refractory focal epilepsy and Engel class 1 surgical outcomes showed that ESL accuracy, indexed by the distance from the nearest surgical margin to the location of a single fit inverse model, improved by around 2 cm from a 31 to a 63 electrode set-up, with little change from a 63 to a 123 electrode set-up (Lantz et al., 2003a). While source orientation was not considered and single-spike peaks were modeled, this is, perhaps surprisingly, the first study to have systematically examined this issue in a well-defined patient group. The question of optimum scalp electrode number for ESL may be settled by default with the future development of quicker methods that reliably fix high-density electrode arrays to the scalp. Until then, more studies in the manner of Lantz et al. are needed. Coregistration problems are more readily overcome with the recent availability of MRI compatible electrodes and the ability to perform less artifact-laden EEG recordings in the MRI scanner. Newly developed coregistration methods, based on the use of mutual three-dimensional virtual landmarks from patient MRI datasets, show promise (Fuchs et al., 2007) but await clinical validation. The computer technology, on which current-generation ESL relies, has become more accessible to clinicians in the last five years due to improvements in the user interface of software operating systems. (F) Distributed models display sources as current density field maps that are closer to reality than dipole modeled sources This, understandably, is not an uncommon misconception. The distributed-ness of a distributed algorithm s solution is generally not the direct representation of the potential field of the actual source. This only holds if the distributed model is perfectly correct, which is virtually never the case. In fact, much of the apparent field effect results from the distributed algorithm s inexactness in modeling the source. Some of this modeling error can be limited by preconstraining the ESL solution to anatomically meaningful boundaries, such as the cortex, a benefit carried by distributed over dipolar modeling methods (Wagner et al., 2001). However, by virtue of their under-determined nature, distributed algorithms are computationally more demanding such that, for most present applications, ESL solutions can only be calculated for time instants. This means that it is difficult to appreciate the relative timing of overlapping source components contributing to the modeled cortical activity across the early spike interval (Scherg et al., 1999). Also, for distributed ESL solutions to be sufficiently spatially resolved, current density thresholds are set which, not unlike fmri signal thresholds, are typically arbitrary. Hence, the more established anatomical pathways of interictal and ictal discharge propagation are not factored into current density threshold settings. CLINICAL STUDIES IN DIPOLAR AND DISTRIBUTED ESL Despite the clinically based research efforts in ESL by Ebersole and others over the last two decades, Krauss and Webber still have it that digital EEG has not significantly expanded the clinical role of EEG, with the possible exceptions of ambulatory monitoring EEG and OR/ICU EEG (Krauss & Webber, 2005). While their premise that an expanded clinical role for digital EEG may depend partially on validating advanced analysis techniques, e.g. modeling seizure sources, seems reasonable enough, does their implicit observation on the clinical worth of ESL still hold, particularly in light of the work carried out in the field in the last few years? What recent progress has been made toward the clinical validation of ESL? We explore some fundamental questions that warrant closer scrutiny if ESL is 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 scalp EEG field was from spike onset to spike peak. They based their observations on the spike-averaged recordings of 16 patients with symptomatic focal epilepsy. All had an Engel class 1 surgical outcome. Using a novel spatiotemporal cluster analysis technique, they saw, on average, three different voltage field maps during the rising phase of the scalp-recorded spike per patient (range one to five). When ESL was performed on these different voltage maps, the source model location coincided with the MRI lesion location for all patients within a fairly narrow time window across the upswing phase of the spike around the halfway point. Either side of this point, the authors argued that ESL results were contaminated by noise (toward spike onset) and by propagation effects (toward spike peak). It should be noted that 125 electrodes were used in the study, so the application of a half-way point rule to ESL when fewer electrodes are employed, as is commonly the case, is not entirely clear. Also, because the inverse model used was a single fit applied to a combined dipolar-distributed algorithm, EPIFOCUS (Grave de Peralta Melendez et al., 2001; Lantz et al., 2001) for each field map, spatiotemporal relationships between successive, independent time-point fits cannot be fully resolved. Finally, as all spikes were averaged, the generalizability of the results to single-spike

9 209 EEG Source Localization in Focal Epilepsy modeling is uncertain. Still, the study is the first to systematically quantify the increasingly appreciated concept that the surface voltage topography, and by extension the ESL result, shifts on a scale of tens of milliseconds across the earliest phase of spike interval. How well does ESL corroborate epilepsy surgery findings? The largest prospective study to date on this topic (Boon et al., 2002) looked at the contribution of spatiotemporal dipole modeling to the clinical decision making process in 100 presurgical patients with refractory focal epilepsy. Most cases were lesional (83%), with the largest subgroup having unilateral hippocampal sclerosis (53%). Scalp ictal EEG recordings from a 27-electrode set-up (10 20 positions plus three inferior temporal electrode pairs) were analyzed. From the 93 patients who recorded ictal EEG phenomena, 62 patients could not undergo ESL analysis due to excessive artifact contamination. Of the remaining 31 patients, it was concluded that ESL influenced the clinical interpretation in 14 cases, usually by confirming the incongruence between structural abnormality and ictal EEG abnormality (10 patients) and leading to the decision not to proceed with invasive EEG recording and further surgical resection. Unfortunately, the study contains several methodological deficiencies. It was not blinded, it gave no information on postsurgical outcome, and it performed zero-phase shift filtering on the EEG raw data. The spatiotemporal modeling was actually limited to the use of a single (regional) dipole, thus making it difficult to disentangle interlobar or interhemispheric propagation effects from effects potentially attributable to multiple independent sources on ESL outcome. Also, ESL results were strictly categorized as type 1 (vertical) and type 2 (radial) dipoles, based on the earlier observations of Ebersole and Wade, who equated the 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 subsequently seen as an oversimplification by Ebersole himself, recognizing the inter-changeability of type 1 and 2 dipolar patterns in both forms of TLE (Ebersole, 2000), largely by virtue of discharge propagation effects occurring early in the interictus, and even earlier in the ictus. The clinical immediacy of this problem was reemphasized by a recent depth electrode study that showed that postsurgical success in medically refractory TLE relies heavily on the spatial resolution of the ictal onset zone on a sublobar scale (Chabardes et al., 2005). Lastly, the authors quoted an 8 h time cost for the analysis from start to finish, an experience that contradicts recent findings on the relative clinical utility 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 supplementary 128 channel surface recording for the purpose of single source dipolar-distributed modeling (EPIFOCUS). Of the 32 patients who had an identifiable focus, seven of whom underwent invasive recording in addition to the routine presurgical work up, all but two patients had concordant ESL findings at a lobar level. From a subgroup of 24 patients who underwent surgery (17 temporal, seven extratemporal), 18 had an ESL maximum that fell within the border of the nearest resection margin (three temporal, three extratemporal were nonconcordant). An Engel class 1 outcome was shared by 16 of the 18 cases (mean follow up 19 months, range: 7 33 months). Interestingly, two of the nonconcordant extratemporal cases were mirror localized to the contralateral hemisphere, their respective presurgical MRI lesions sitting close to the parieto-occipital midline. Although the intracranial and 128 channel recordings were not performed simultaneously, the investigators quoted a high level of agreement between the intracranially directed interictal and ictal localization and the high-density surface electrode-directed ESL result (five of seven cases). What is especially striking about this study is the degree of accuracy achieved for the localization despite the fact that each patient s MRI brain was morphed to fit a threeshell sphere for the forward model set-up, with standardized electrode positions prefitted to the outermost shell. ESL results were constrained to the cortical gray matter and based on the midway point of the averaged spike s upswing phase. While the results are encouraging, and each ESL analysis was performed in a timely manner, there is the concern that the investigators were not blinded to the patients para-clinical data during the source fitting procedure. Comparative results on normal (mock) MRI data would have further strengthened the case for the robustness of their source localization technique. Also, as the investigators do point out, resection boundaries are variably wider than lesion boundaries and so measurement bias may have inflated the accuracy of their ESL results. Rather than countering this point by stressing the ESL concordance for scalp and intracranial recordings in the few patients who had both performed, a breakdown of the distance from nearest resection boundary to nearest lesion boundary in each of the surgical cases may have been more informative. The same research group (Sperli et al., 2006) more recently examined ESL accuracy in a pediatric epilepsy surgical cohort (13 temporal, 17 extratemporal). Interictal EEG recordings were acquired using scalp electrodes. A distributed inverse model was applied in this case (depth-weighted minimum norm, MN). The MN algorithm favors current density solutions that explain surface electromagnetic fields with the least net strength per time point (Hamalainen & Ilmoniemi, 1994). MN solutions therefore typically localize to the superficial cortex and a mathematical depth weighting term is often applied to counteract

10 210 C. Plummer et al. this tendency (Michel et al., 2004a). Presumably as a result of the blurred, diffuse nature of the MN-based solution, ESL accuracy was determined in this study by the degree of overlap (arbitrary 50% minimum) between the resection boundary (defined as the epileptogenic region ) and a statistically deconstructed depth-weighted MN map (earliest vowel-wise activity p < vs. background), which the authors defined as the region of discharge onset. The results were encouraging with a 90% concordance between ESL location and nearest resection border and an 87% postoperative seizure freedom rate (mean follow up 13 months, range: 2 24 months). The authors argued that the three mislocalized cases (all temporal) stemmed from the inadequate sampling of the inferior temporal region, a premise that was supported by their corrected modeling of two of these cases when ESL was repeated with a highdensity electrode set-up (128 channels). As with their previous paper, there was no blinding in this study. In a majority of the cases (19/30), no postoperative MRI was available and the epileptogenic region was mapped via an interpretation of the surgical notes. This study draws out some of the inherent difficulties associated with ESL based on current-generation distributed modeling methods. With solutions that are as visually seductive as functional MRI (Ebersole, 1997a), it should be appreciated that distributed models are based on complex sets of mathematical assumptions that are yet to achieve clinical validation. The use of a statistical discriminator in the present study might be seen as a step in the right direction in this validation process. While the present results need to be reproduced by other investigators, the level of accuracy obtained with a electrode set-up does support a potential role for ESL in the routine clinical setting. Finally, it should be stressed that studies using lesion or resection margins to validate ESL results, even in Engel class 1 cohorts, should be interpreted cautiously. The relationship between the epileptogenic zone and the putative epileptogenic lesion is far from direct for review see Rosenow & Luders (2001). How well does scalp ESL model interictal and ictal onset as defined by intracranial recordings? Zumsteg and colleagues also performed statistical postprocessing of a distributed algorithm (LORETA) in a retrospective study of 15 patients with symptomatic mesial TLE (MTLE) (Zumsteg et al., 2005). Unlike the study by Sperli and colleagues (Sperli et al., 2006), they used nonparametric mapping (SNPM), thereby avoiding an assumption that the raw ESL results necessarily conformed to a Gaussian statistical distribution. They compared the interictal localization suggested by recordings from foramen ovale (FO) electrodes, which look directly at the hippocampus, with both the raw and SNPM LORETA results derived from the simultaneous 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 ESL based on the rising phase of the spike-averaged waveform. Raw LORETA maps typically showed basolateral temporal activation, while the corresponding SNPM LORETA images showed more discrete, mesially placed activation that correlated well with the local FO field. The decision to exclude 11 patterns from the LORETA analysis due to the suspected nonfocality of the source (based on the local FO field) seems unusual. One of the benefits of distributed over dipolar modeling is that the former is generally better equipped to display extended source configurations. Also, while the authors tabulated Engel class surgical outcomes at one year for each patient (most were class 1), they did not indicate which FO pattern belonged to which patient. In a follow-up study (Zumsteg et al., 2006), the investigators used the same patients and the same SNPM LORETA technique to explore the nature of spike propagation in MTLE. However, all 30 FO field patterns were included in this study (19 mesial from the earlier study, and 11 lateral ). Based on the SNPM LORETA activation sequence, signal propagation was evident in 16 patterns, occurred in either direction (mesial to lateral, lateral to mesial), preceded the spike peak, and was not associated with Engel class outcome. While the authors noted several limitations in the retrospective study design (reliance on FO electrodes to capture large propagating fields, use of a three-shell forward model coregistered with a generic brain MRI, and use of only two supplementary inferior temporal electrode pairs), they regarded it as the first attempt to examine the accuracy of a distributed inverse model using simultaneously acquired scalp and intracranial EEG recordings. A different group of investigators (Nayak et al., 2004) used dipole modeling to help characterize the relationship between the FO recorded field and the corresponding scalp EEG field in a retrospective study of 20 patients with MTLE. Several important findings came from their meticulous analysis of over 4,000 FO spikes. Only 9% of FO spikes were identified de novo at the scalp. Otherwise, either scalp signal averaging (60%) or FO spike correlation (13%) was needed to confidently identify the corresponding lower voltage scalp spikes (no scalp signal was seen despite such EEG postprocessing in the remainder, 18%). Interestingly, de novo scalp spikes were associated with a shallower FO field gradient and were seen up to 2 msec later than the small scalp spikes. The latter were associated with a wider scalp field with around 20% of these peaking in amplitude at the contralateral scalp. Dipole localization placed de novo spikes at the retro-orbital region and smaller spikes at the mesial temporal region, but there dipole orientation was more haphazard. The authors reasoned that the de novo spikes (100+ microv) were the product of summated propagation from the deeper mesial source configuration and that the smaller

11 211 EEG Source Localization in Focal Epilepsy spikes (20 40 microv) were the product of volume conduction. The implication here perhaps is that dedicated identification of low-amplitude spikes in MTLE can provide very useful ESL information that is potentially more physiologically plausible given the fact that dipole modeling is fundamentally reliant on the principles of volume conduction. A criticism of this aspect of the study however, was the use of a single fixed dipole and a single shell model. While acknowledging that this simplified modeling set-up was not designed to interrogate source behavior at the neurophysiological level, the authors did nonetheless go on to discuss their ESL findings at this level. For instance, it was argued that the retro-orbital dipole localization was likely the result of distortion of the scalp EEG field with the preferential propagation of current through the superior orbital fissures. However, it is also quite possible that their use of a modified (Maudsley) electrode array (Margerison et al., 1970), which gives electrodes a better view of inferior brain convexities, effected downward displacement of the dipole. When combined with the anticipated opposing effect of spherical forward modeling in MTLE on dipole localization (upward displacement to the frontal lobe), this might well result in partially compensated mislocalization to the retro-orbital region. For ease of comparison, spike peaks were also modeled. If de novo spikes were the product of discharge propagation, then confining ESL to the spike peak may well exaggerate the influence of signal propagation on the final result. Indeed, as the authors argue, the many discharge patterns captured at the scalp relative to the deeper FO field were the probable manifestation of a continuum of source behavior effects from discharges seen only by the FO electrodes, to those seen more globally by virtue of volume conduction, to those seen slightly later at the scalp by virtue of signal propagation. The probing of such spatiotemporal effects with a more sophisticated forward inverse modeling set-up would have been very worthwhile, particularly in light of the generally held view that deep mesial discharges are only seen at the scalp by virtue of propagation alone (Ebersole, 2003a). Finally, the authors did not show patient clinical data (radiology, pathology, and surgical outcome), which should have been available, the presurgical work-up having been performed between 1990 and Nevertheless, this work demonstrates the value of using lower voltage spikes to study interictal source behavior in MTLE and it challenges the view that MTLE scalp spikes are the exclusive byproducts of cortico-cortical propagation from deeper mesial structures. Relatively few ictal studies addressing the correlation between scalp and intracranial ESL have ever been published and, much like the previously described interictal work, most studies have used presurgical TLE patient cohorts. The main ictal studies are limited to dipole modeling and arguably the most influential publication is 10 years old (Assaf & Ebersole, 1997). The authors examined the EEG recordings of 40 TLE patients who required intracranial electrode implantation as part of the surgical work-up. All patients had an Engel class 1 outcome with a mean follow-up of one year. Spatiotemporal dipole modeling was carried out on scalp data that had been acquired with a 25-electrode set-up (standard set-up plus three inferior temporal electrode pairs). Dipoles with different orientations were preassigned to model different sublobar divisions of the temporal cortical surface. The investigators selected the dominant and/or leading dipole model that explained the earliest recognizable, averaged ictal rhythm seen at the scalp. Dipole models were then matched with the ictal localization suggested by the intracranial recordings and expressed as positive predictive values. The results were impressive, with high positive predictive values found for the following source and seizure onset match-ups: vertical tangential dipole (basal source) and hippocampal onset (89%), horizontal tangential dipole (temporal tip source) and entorhinal onset (83%), horizontal radial dipole (lateral source) and neocortical onset (80%). Multiple source components were modeled in 13 patients in whom an oblique dipole model ( geometric mean of above three source components) was thought to explain seizure onset at the inferolateral temporal cortex. As the authors indicated, the ictal ESL results are largely in agreement with previous interictal ESL results in TLE. This is perhaps not surprising given the relatively good correlation between interictal and ictal lateralization in TLE (Blume et al., 2001). It should be noted that, as a retrospective study, scalp and intracranial EEG recordings were not simultaneously acquired and the authors did not indicate that they were fully blinded to the intracranial data when scalp ESL was performed. Also, many seizures were captured (212 in total) but the statistical analysis was only done on a patient-wise basis. Notwithstanding the Engel class 1 outcome for all patients, a more rigorous analysis of the consistency of dipole modeling for each seizure in each patient would have been useful. Moreover, the investigators chose the dipole fit that best explained the early ictal rhythm but did not describe the adequacy of the best fit in more quantitative terms. For example, it is unclear how well the dipole model explained the signal variance in each case, or how well the leading dipole model dominated the second dipole fit when their respective source components overlapped spatiotemporally. In a related publication (Assaf & Ebersole, 1999), the authors showed how their dipole modeling approach might be used to anticipate surgical success following either standard or modified anteromesial temporal lobectomy (AMTL). After a minimum follow-up period of two years, they found that postsurgical seizure freedom was more likely to occur in patients whose dipole modeling suggested a dominant or leading basal source, but was

12 212 C. Plummer et al. most likely to occur in patients whose dipole modeling did not implicate a lateral source. The latter association was explained on the basis that neocortical foci are unlikely to be resected adequately by AMTL, even in the modified approach when the lateral surgical margin is extended. Of the few ictal ESL studies that have compared simultaneously acquired scalp and intracranial EEG in focal epilepsy, it is difficult to look past the methodical study by Merlet and Gotman in which the accuracy of spatiotemporal dipole modeling from scalp EEG (28 40 channel setup) was judged in relation to the localization suggested by depth and/or epidural electrode recordings (Merlet & Gotman, 2001). A consecutive series of 15 presurgical patients with various forms of refractory partial epilepsy were enrolled in the study. Patients were excluded from ESL if they did not have at least two reproducible seizure patterns recorded at the scalp, one following the other. By specifying this condition, the investigators are the first to have systematically examined the spatial resolution of dipole localization as the seizure pattern evolves at the scalp electrodes. Of the nine patients (seven lesion-negative) who met this condition, six patients (five lesion-negative) had averaged ictal patterns that could produce a sufficiently stable and interpretable ESL result. As noted by other investigators, the earliest ictal rhythm seen at the scalp was always associated with intracranial discharges occupying large areas of cortex. In a new finding though, the three patients with unstable ESL results had, by the time an ictal rhythm was seen at the scalp, an intracranial ictal rhythm that was bilateral with maximal amplitude at the mesial temporal region. Further, of the six patients who returned a stable ESL result at some point during the seizure, only half had scalp EEG changes that were concomitant with seizure onset as suggested by the intracranial recording. The dominant source in each case implicated a neocortical temporal focus with distances from the main dipole to the nearest (maximal amplitude) intracranial electrode as follows: 5.5 mm, 8.3 mm, and 20.8 mm. Two of these patients, and two others (four of six), had dominant dipoles that coincided with the locations of intracranially recorded voltage maxima during the earlier ictal pattern, but this was only the case for two of the six patients when the second (later) ictal pattern was modeled. While this study represents one of the most carefully conducted analyses of ESL to date, the interpretation of the findings was perhaps slightly misdirected. Much emphasis was given to the physical separation of the dominant dipole to dominant electrode in trying to validate the scalpderived ESL result. The authors did point out that their quoted distances should not be regarded as error margins, but rather as measures of concordance. However, measures of spatial inconsistency for ESL results ranged from 15.3 mm to 38.4 mm. These distances are modest when one considers the following factors: that intracranial EEG localization carries an inherent error due to the problem of under-sampling the cortical garland; that dipoles model cortical surfaces in centimeter dimensions; that dipole orientation carries far more weight than dipole location in modeling source origin and/or propagation; and that the amplitude of the ictal discharge, on which the measurements were based, is highly dependent on the orientation of the actual source as it faces the recording electrodes. To illustrate, the authors concluded that mesial onset seizures are prone to mislocalization by scalp ESL based on their concordance measurements from the amygdala (maximal intracranial signal) to the anterior temporal region (dominant dipole location) in two patients (20.8 mm and 38.4 mm). However, such distances are not unexpected in light of the recognized patterns of ictal and interictal propagation in MTLE. More useful would have been a clear description of dipole orientation for the spatiotemporal models applied to each ictal pattern for these two patients, but ideally for all patients. Because scalp-derived ESL localization is more likely to reflect discharge propagation, versus discharge onset, assessing ESL spatial accuracy by location of the intracranial EEG maxima is problematic. Once again, it is the orientation of the dipole and the extended area of cortex to which that dipole projects that yields a truer indication of ESL validity in modeling ictal or interictal patterns of onset and propagation. Lastly, the investigators used a relatively low high-pass filter setting (0.3 Hz) and applied start markers for their ictal modeling at a latency well before any deflection was noted in the scalp trace (based on a figure supplied). It is therefore possible that early ictal signal quality may have been compromised for ESL, leading to noisier, less-stable dipole solutions. It should be emphasized here that the use of intracranial EEG recordings as a gold standard validation tool for ESL is problematic. However meticulously conducted, scalp-intracranial EEG studies, such as those discussed above, are necessarily limited by the extent to which the cortex is sampled at depth. For instance, FO electrodes only sample the entorhinal cortex and orthogonal depth arrays, as used in the last study (Merlet & Gotman, 2001), have a limited view of the temporal neocortex. Separating source origin from propagation will, in such circumstances, involve a measure of speculation on the part of the investigator. The above ESL findings await replication, ideally with the use of electrode arrays that sample larger areas of cortex; as with sampling that sufficiently includes mesial, inferior, anterior tip, and lateral temporal lobe cortical surfaces in the case of TLE interictal and ictal ESL. In fact, without such studies, the capacity for ESL to help characterize the interplay between the lesion, irritative zone, the seizure onset zone, and the epileptogenic zone will remain untested.

13 213 EEG Source Localization in Focal Epilepsy OTHER NONINVASIVE SOURCE LOCALIZATION METHODS If publication output is anything to go by, MEG and EEG-fMRI have attracted greater research interest than that enjoyed by ESL in recent times. Signal acquisition techniques have improved for both methods, which promise better spatial resolution than that offered by currentgeneration ESL. For MEG, modern high-density sensor arrays that encompass the whole head, and not just part of it, have allowed recordings to be done in a single step, rather than in a cumbersome, piece-wise manner. For EEGfMRI, novel ways of recording EEG in the hostile MRI environment have given researchers the chance to better understand how cortical and subcortical hemodynamic response patterns are coupled to scalp recorded spike and seizure patterns. For ESL then, there are two immediate questions. Has ESL been marginalized by MEG? It should be emphasized that the principles of signal detection and source localization for EEG apply just as equally to MEG. That is, signal detection in MEG depends on the recruitment of a sufficient population of discharging cortical neurons that are synchronized in time and aligned in space. And for source localization in MEG, both the forward and inverse problems need to be resolved with suitable models to achieve a tenable solution. This fact that MEG-based source localization is answerable to the same kind of fundamental mathematical problems that underpin ESL is easily overwhelmed by the glare of the technology on offer. MEG technology is seductive but expensive. Nunez puts it another way. Enthusiasm for MEG (relative to EEG) has been boosted by both genuine scientific considerations and poorly justified commercial pressures (Nunez & Srinivasan, 2006b). Even if MEG becomes more portable and affordable at some future date, ESL is likely to maintain an important stake in noninvasive source modeling. This is because, apart from the obvious practical advantages currently held by EEG over MEG (pediatric and ictal studies, long-term monitoring are largely prohibited by head movement artifacts), EEG and MEG see spike and seizure discharges quite differently. The practice of pitting MEG against EEG in the source imaging literature has perhaps been overdone, and there is now an evolving consensus that the combined use of these techniques (in the rare situation when both are accessible) optimizes source localization accuracy (Fuchs et al., 1998; Barkley & Baumgartner, 2003). While comparisons between the two methods will continue to be made, it should be noted that to date no study has been published which compares source modeling accuracy for simultaneously acquired MEG and EEG recordings against simultaneously acquired intracranial data (as the surrogate gold standard) in a prospective, blinded manner for focal epilepsy. A common misconception in this regard is that MEG is more accurate than EEG in defining source activity owing to its superior spatial resolution and its relative immunity to field distortion by volume conductor effects (Nunez & Srinivasan, 2006b). While these latter qualities are true enough, MEG only picks up part of the cortical activity generated by the source(s). To coin an analogy, one might imagine that if a patch of discharging cortex is likened to a 10-cm 2 piece of undulating cheese, EEG s field of view will be blurred and distorted by the overlying cellophane wrapper, while MEG s view will be clearer, but restricted, as if the cheese had been made Swiss. This is because MEG is blind to the radial vector component of the electric field. Thus, the magnetic field is less complicated by variably admixed radial and tangential vectors and it is less distorted by the skull scalp interface. However, MEGbased modeling has an inherent bias for superficial sources because the magnetic field decays very rapidly from scalp surface (Hamalainen et al., 1993). It is interesting to note that MEG source imaging (MSI) researchers have almost exclusively applied single fixed dipoles to model spikes in focal epilepsy. While the use of such a simple inverse algorithm in MSI might better suit the modeling of the cleaner signal topography of magnetic versus electrical fields, it is a mistake to think that MEG is immune from the same properties of signal propagation as EEG. Gloor puts it bluntly. Modeling the magnetic field based on the assumption that the field can be represented by a single fixed dipole is fraught with difficulties similar to those inherent in the modeling of electrical fields based on this assumption (Gloor, 1985). Therefore, the interpretation of MEG source localization accuracy on the millimeter scale, as is the usual case in the MSI literature, should be eyed with at least a degree of caution. Indeed, the area of cortex required for contemporary MEG arrays to detect an interpretable field is still in the order of 3 cm 2 at best (Oishi et al., 2002). A recent paper by Fischer and colleagues has looked to redress this issue with the calculation of ellipsoid volumes based on dipole cluster variability 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 gyral crests), most of the cortex is seen by MEG arrays because, as observed by Brodmann nearly 100 years ago, the cumulative gyral surface only accounts for a third of the total surface area of the human brain (Brodmann, 1909). But the implication here is that anatomy and physiology are measured with the same ruler. As Wong reminds us, cortical lamination, pyramidal arborization, cortical vascularization, and cortico-cortical connections are richer at gyral crowns than at fissural walls (Wong, 1998). Gyri also account for much of the cortical homunculus in man (Welker,

14 214 C. Plummer et al. Figure 4. Demonstration of the relationships between cortical anatomy, surface electrical field, and dipole model for a right frontotemporal spike in a patient with focal epilepsy. T 1 -weighted MRI coronal (A) and sagittal (B) views; and surface rendered cortical images, oblique (C) and from above(d), are shown. Note that dipoles (rotating over 40 msec epoch) and the associated confidence ellipsoid volumes do not conform to either the position or orientation of a particular sulcus, gyrus, or even lobe (A, B). Rather, dipoles model the summated electrical field recorded at the scalp 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 modeling of 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 sulcal surfaces) that is ultimately modeled by the dipole solution. Abbreviations: MRI (Magnetic Resonance Image). Epilepsia C ILAE 1990; Wong, 1998). Much of the immediate fissural region is dedicated to propagation of interictal/ictal discharges, a phenomenon that is generally ill suited to single fixed dipole modeling strategies as discussed earlier. This is especially so if modeling is restricted to the spike peak, a practice commonly adhered to in MEG epilepsy studies. The redundancy of the anatomical two-thirds argument is made clearer when it is recalled that dipoles are not anatomically based constructs, but representations of the summated electrical field generated by areas of cortex large enough to include both gyral and fissural surfaces (Fig. 4). Despite these caveats, support for MSI as a legitimate source localization technique has grown. Many clinical studies, most of which are understandably interictal, have found favorable correlations between MSI location and the localization suggested by either intracranial recordings or postsurgical seizure recurrence rates for keynote studies see (Stefan et al., 2003; Pataraia et al., 2004) and