Mapping the Signal-To-Noise-Ratios of Cortical Sources in Magnetoencephalography and Electroencephalography

Transcription

1 Human Bain Mapping 30: (2009) Mapping the Signal-To-Noise-Ratios of Cotical Souces in Magnetoencephalogaphy and Electoencephalogaphy Daniel M. Goldenholz, 1,2 * Seppo P. Ahlfos, 1,3 Matti S. Hämäläinen, 1,3 Dahlia Shaon, 1 Mamiko Ishitobi, 1 Lucia M. Vaina, 1,2,4 and Steven M. Stufflebeam 1,3 1 Athinoula A. Matinos Cente Fo Biomedical Imaging, Massachusetts Geneal Hospital, Chalestown, Massachusetts 2 Biomedical Engineeing Depatment, Boston Univesity, Boston, Massachusetts 3 Havad-MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambidge, Massachusetts 4 Depatment of Neuology, Havad Medical School, Boston, Massachusetts Abstact: Although magnetoencephalogaphy (MEG) and electoencephalogaphy (EEG) have been available fo decades, thei elative meits ae still debated. We examined egional diffeences in signal-to-noise-atios (SNRs) of cotical souces in MEG and EEG. Data fom fou subjects wee used to simulate focal and extended souces located on the cotical suface econstucted fom high-esolution magnetic esonance images. The SNR maps fo MEG and EEG wee found to be complementay. The SNR of deep souces was lage in EEG than in MEG, wheeas the opposite was typically the case fo supeficial souces. Oveall, the SNR maps wee moe unifom fo EEG than fo MEG. When using a noise model based on unifomly distibuted andom souces on the cotex, the SNR in MEG was found to be undeestimated, compaed with the maps obtained with noise estimated fom actual ecoded MEG and EEG data. With extended souces, the total aea of cotex in which the SNR was highe in EEG than in MEG was lage than with focal souces. Clinically, SNR maps in a patient explained diffeential sensitivity of MEG and EEG in detecting epileptic activity. Ou esults emphasize the benefits of ecoding MEG and EEG simultaneously. Hum Bain Mapp 30: , VC 2008 Wiley-Liss, Inc. Key wods: MEG; EEG; epilepsy; fowad model; SNR; simulation; dipole; cotex; bain INTRODUCTION Contact gant sponso: National Cente fo Reseach Resouces; Contact gant numbe: P41RR14074; Contact gant sponso: NIH; Contact gant numbes: NS37462 and NS44623; Contact gant sponso: Mental Illness and Neuoscience Discovey (MIND) Institute. *Coespondence to: Daniel Goldenholz, MGH-Matinos Cente, Building 149, 13th St., Chalestown, MA daniel@nm.mgh.havad.edu Received fo publication 13 Febuay 2007; Revised 17 Septembe 2007; Accepted 22 Febuay 2008 DOI: /hbm Published online 8 May 2008 in Wiley InteScience (www. intescience.wiley.com). Although a gowing numbe of clinical methods ae available fo obseving bain activity noninvasively, only two povide millisecond scale tempoal esolution with centimete o bette spatial esolution: electoencephalogaphy (EEG) and magnetoencephalogaphy (MEG). Despite being geneated by simila neual souces, MEG and EEG signals diffe, suggesting that ecoding both simultaneously is beneficial [Babiloni et al., 2004; Bakley and Baumgatne, 2003; Iwasaki et al., 2005; Knake et al., 2006; Leijten et al., 2003; Lesse, 2004; Lin et al., 2003; Lopes da Silva et al., 1991; Pataaia et al., 2005; Wood et al., 1985; Yoshinaga et al., 2002; Zijlmans et al., 2002]. Howeve, patly VC 2008 Wiley-Liss, Inc.

2 Goldenholz et al. due to the highe cost of an MEG system, neuophysiologists and clinicians ae hesitant to adopt the technology, citing studies that conclude the diffeences in the souce localization accuacy between MEG and EEG ae mino [Baumgatne 2004; Cohen et al., 1990; Liu et al., 2002]. Convesely, those with expeience in MEG often do not ecod EEG simultaneously, elying on evidence of MEG s moe pecise localization capabilities [Hämäläinen et al., 1993; Leahy et al., 1998], theeby educing substantially the pe-ecoding pepaation time equied fo each subject. An impotant potential application of MEG and EEG souce localization is the pesugical evaluation of epileptic patients. Epilepsy affects up to 1% of the population in Noth Ameica [Wiebe et al., 2001]. Appoximately 20% of patients do not achieve adequate contol of seizues with medication; many of them ae candidates fo sugey that can educe o eliminate seizues [Pataaia et al., 2002; Wiebe et al., 2001]. Sugical outcomes ae impoved by identifying and locating epileptic spikes in MEG and/o in EEG [Ebesole and Pedley, 2002; Stefan et al., 2003]. An epileptic spike must exhibit high enough signal-to-noise-atio (SNR) to be distinguished fom backgound noise [Cobb, 1983; Iwasaki et al., 2005], and to be localized accuately [Fuchs et al., 1998; Takiainen et al., 2003]. The SNR of obseved bain activation in EEG and MEG depends not only on the senso chaacteistics (type, placement, noise), but also on the location and oientation of the souce. A numbe of studies have examined sensitivities and expected SNR values of MEG and/o EEG [de Jong et al., 2005; Fuchs et al., 1998; Hilleband and Banes, 2002; Takiainen et al., 2003]. The pesent study extends and expands on pevious wok by incopoating the ealistic shape of the cotical suface to impose an anatomical constaint on the locations and oientations of the souces. We examined the diffeence in SNR between MEG and EEG fo each cotical location using both actual noise ecodings and a simple noise model to bette undestand the genealizability of the esults. We also compaed the SNR of individual dipole souces to that of extended cotical patch souces, and elated ou findings to the diffeential detection of epileptic spikes in MEG and EEG. METHODS Data Acquisition Fou ight-handed subjects (two females), aged 23 42, wee included in this study. Each signed a consent fom and a pivacy statement in accodance with ou Institutional Human Subject Reseach Boad and HIPAA standads. High-esolution anatomical magnetic esonance images (MRI) wee obtained with a Tio 3T scanne [Siemens Medical Systems, Elangen, Gemany] using MPRAGE pulse sequences (voxel size mm 3 ; slice thickness 1.3 mm; TE 3.31 ms; TR 2530 ms; gap 50%; FOV 10 cm 3 10 cm) and FLASH images (flip angle 58; slice thickness 1.3 mm; FOV 10 cm 3 10 cm). MEG and EEG data wee acquied simultaneously with a Vectoview TM system [Elekta-Neuomag, Helsinki, Finland] fom 204 plana gadiometes, 102 magnetometes, and 70 EEG electodes (see Fig. 1). Two minutes of spontaneous activity was ecoded, sampled at 600 Hz with hadwae filtes set at Hz (Subjects 1 and 2) o Hz (Subjects 3 and 4). The EEG data was tansfomed to the aveage electode efeence. Signal-space pojection (SSP) was applied to magnetomete data [Tesche et al., 1995] to educe backgound envionmental noise. The noise subspace fo SSP was selected as the thee-dimensional space spanned by the eigenvectos coesponding to the thee lagest eigenvalues of the coelation matix of a 5-min ecoding of data collected without a subject pesent; these coespond appoximately to the homogeneous field components, typically oiginating fom envionmental (e.g., moving vehicles) souces fa fom the sensos. The locations of fiducial head points, EEG electodes, and head-position indicato (HPI) coils wee digitized using a FastTack 3D digitize [Polhemus, USA]. The location of the MEG senso aay with espect to the head was detemined at the beginning of each measuement fom the magnetic fields geneated by the HPI coils. The Fowad Model Fo fowad modeling, we used a linea collocation thee-laye bounday-element method with conductivity values: 0.3, 0.06, and 0.3 S/m fo the bain, skull and scalp, espectively [Hämäläinen and Savas, 1989; Hämäläinen et al., 1993]. The suface of the skin, as well as the inne and oute sufaces of the skull wee detemined fom the MRIs (see Fig. 1). Each suface was tessellated with 5120 tiangles, poviding adequate numeical accuacy [Couzeix et al., 1999; de Jongh, 2005; Fuchs et al., 2001; Takiainen, 2003]. The cotical suface was econstucted fom the MRI data with about 130,000 vetices pe hemisphee and an appoximate vetex-to-vetex spacing of 1 mm using the Feesufe softwae [Dale et al., 1999; Fischl et al., 1999a; Fischl et al., 2001; Segonne et al., 2004]. We computed the MEG and EEG signals pedicted by the fowad model fo dipole souces oiented pependicula to the gay-white matte bounday at each of the suface vetices. Neual souces wee assumed to be eithe point souces (cuent dipoles) o synchonously active patches. Fo dipola souces, the souce amplitude was 10 nam; fo patches, souce stength was chosen to coespond to a unifom suface souce density of 50 pam/mm 2 [Hilleband and Banes, 2002; Lu and Williamson, 1991]. To ceate extended souces, the cotical suface was divided into two complete sets of centoids using the geodesic distanceweighted Dijksta algoithm [Dijksta, 1959]. The souce patches had geodesic adii of 10 mm o 16 mm, coesponding to aeas of 3 cm 2 o 8 cm 2, espectively. 1078

3 SNR Maps fo MEG and EEG Figue 1. Senso and suface configuation. A: The locations of the MEG senso aay with espect to the head (Subject 2). B: The locations of the EEG electodes on the scalp. C: Suface endeing of the ceebal cotex with the scalp endeed semi-tanspaent. The second ow shows the tessellations of the scalp (D), the oute suface of the skull (E), and the inne suface of the skull (F) employed in the bounday-element fowad model. These sufaces wee econstucted fom the stuctual MRI. [Colo figue can be viewed in the online issue, which is available at Signal-To-Noise Ratio Fo Individual Souces We defined the SNR fo each souce location (dipole o extended patch) in decibel units as: $ % a 2 X b 2 k SNR ¼ 10 log 10 ; ð1þ N whee b k is the signal on senso k povided by the fowad model fo a souce with unit amplitude, a is the souce amplitude, N is the numbe of sensos, and s 2 k is the noise vaiance on senso k. Fo the compaison of MEG and EEG, the diffeential SNR was defined as k s 2 k D ¼ SNR MEG SNR EEG Note that D is independent of souce amplitude a. If the MEG SNR exceeds that of EEG, then D > 0; in the opposite case D < 0. Fo a combined MEG/EEG SNR map, max{snr MEG, SNR EEG } was selected at each cotical location. Gand-aveage SNR maps acoss the subjects wee computed using a spheical mophing pocedue that is based on aligning sulcal and gyal featues [Fischl et al., ð2þ 1999b]. The pecentage of cotical aea whee the SNR was highe in MEG than in EEG was detemined by computing the faction of souces fo which D > 0. Note that unde the assumption of additive Gaussian noise, aveaging acoss evoked tials is equivalent to adding a constant to all SNR values in MEG and EEG, and thus will not influence the diffeence D maps. Noise Estimation The noise vaiance s 2 k used in the SNR calculations was obtained in two ways. In the fist appoach, the noise vaiance ^s 2 k;ecoded fo each senso was estimated fom MEG and EEG ecodings of spontaneous activity, filteed at Hz. In the second appoach, to educe the influence of idiosyncasies of the MEG and EEG sensos pesent duing a specific session, we also used an altenative noise model based on unifom backgound bain activity. The model assumed a unifom spatial distibution of identical independent noise souces [de Munck et al., 1992], oiented pependicula to the cotical mantle. The amplitudes of the souces wee assumed to have a Gaussian distibution with zeo mean and vaiance s 2 s. Since instumentation noise is typically substantially smalle than signals 1079

4 Goldenholz et al. Figue 2. SNR maps based on the noise model (goup aveage, left hemisphee, plotted on Subject 2 s bain). The colo scale is identical in each SNR map. The SNR estimates plotted on the cotical suface: SNR MEG (fist ow), SNR EEG (second ow), maximum of SNR MEG and SNR EEG (thid ow). Fouth ow: inflated cotical cuvatue maps and folded cotex maps. Convex aeas (gyi) and concave aeas (sulcal walls) ae indicated in light and dak gay, espectively. due to spontaneous bain activity [Ebesole and Pedley, 2002; Hämäläinen et al., 1993], an instumentation noise tem was not included in ou model. Unde these assumptions, the noise vaiance fo senso k is s 2 k ¼ s2 s ðaat Þ kk ; whee A is the fowad solution matix computed fo the noise souces with unit amplitudes, T indicates matix tanspose. Fo the computation of A, we decimated the dense tiangulation of the cotex to an appoximate intesouce spacing of 7 mm. The unknown souce vaiance s 2 s fo the noise model was estimated fom the ecoded activity ^s 2 k. The median values of ^s 2 k;ecoded =ðaat Þ kk wee detemined sepaately fo the MEG gadiometes, the MEG magnetometes, and the EEG channels. Medians wee used to avoid accounting fo outlie, o bad channels. The mean of the thee median values, weighted by the numbe of channels in each ð3þ senso type, was taken as an estimate of s 2 s ; these wee found to be simila fo the fou subjects (squae oot of s 2 s values: 1.6, 1.6, 1.6, and 1.9 nam). Theeafte, senso vaiances ^s 2 k fo MEG and EEG wee calculated with Eq. 3 using that oveall estimate of the souce vaiance s 2 s. RESULTS Signal-To-Noise-Ratio Of Dipole Souces MEG and EEG SNR maps, aveaged ove the fou subjects, ae shown in Figue 2. Fo MEG, the SNR map showed high SNR on most supeficial cotical egions, with lowe SNR in deepe aeas such as sulci on the lateal suface (in paticula, the Sylvian fissue), intehemispheic cotex, and pats of the vental cotex. The SNR was lowest nea the cente of the head. In addition, thee wee thin stips at the cests of gyi with low SNR. These coe- 1080

5 SNR Maps fo MEG and EEG spond to aeas whee the souce oientations ae appoximately nomal to the neaby suface of the skull. Fo EEG, the SNR map is much moe unifom than that of MEG. The SNR of EEG was low in the infeio fontal aeas, pobably due to an inadequate sampling of the scalp potential distibution with the pesent electode aay. The diffeences between the two modalities suggest that combined ecodings can impove the oveall SNR of cotical souces (Fig. 2, thid ow). A diffeence SNR map (D map, c.f. Eq. 2) is shown in Figue 3. Deep aeas, such as much of the medial suface of each hemisphee and the Sylvian cotex, and the cests of the gyi had a highe SNR in EEG. The fontal and occipital poles had a highe SNR in MEG, as well as most egions on the lateal suface. On the basis of the noise model, the aveage aea (acoss subjects and hemisphees, excluding the venticula sufaces) fo which SNR was highe in MEG than in EEG was appoximately 40% of total aea of the cotex. In Figue 2 and the two top ows of Figue 3, the noise model was used. The bottom ow of Figue 3 shows D maps ecomputed using vaiances fo each senso estimated fom ecoded data. The aveage aea of cotex fo which the SNR is highe in MEG than in EEG was 55%, which is somewhat lage than that computed using the noise model. The SNR maps due to ecoded noise in MEG and EEG (not shown) suggest that the additional aea is due to highe MEG SNR values than pedicted by the noise model. Extended Souces MEG/EEG signals ae likely to aise fom extended patches of active cotex [Ebesole and Pedley, 2002]. Figue 4 shows compaison maps geneated based on dipoles and extended souces. The value assigned to a souce location epesents the numbe of subjects fo which D > 0at that point. Fo many supeficial dipola souces, the MEG SNR was consistently highe in all subjects. This patten changed with souce extent: as the extent inceased, the total aea of cotex that has highe SNR in MEG became smalle. Nonetheless, seveal egions had consistently highe SNR in MEG acoss subjects, including occipital pole, infeio occipital lobe, tempoal pole, posteio tempoal lobe, and fontal pole. Clinical Example To illustate the pactical implications of SNR maps, we pesent data fom an epileptic patient. MEG and EEG wee ecoded simultaneously fom a 38-yea-old female with a hypothalamic hamatoma, suffeing fom intactable complex patial seizues since age 2. We obseved fequent epileptic dischages independently fom the left and ight fontal-tempoal egions. We selected two inteictal spikes that appeaed diffeent in MEG and EEG (see Fig. 5). The fist spike (A) was easie to identify in EEG taces, wheeas the second spike (B) was easie to identify in MEG taces. Equivalent cuent dipoles wee localized based on the EEG fo spike A and on the MEG fo spike B. These souce locations wee then examined using a diffeence SNR map between MEG and EEG fo that patient s cotical anatomy to make post-hoc pedictions about the SNR. At the obtained dipole locations, the D values based on ecoded noise pedicted the obseved diffeences between MEG and EEG fo both spikes. In contast, the noise model based D maps (not shown) only pedicted SNR diffeence fo spike A. This suggests that the noise model oveestimated the MEG noise level fo that paticula ecoding session and fo that cotical location. DISCUSSION We employed ealistic cotical geomety models to compae the SNR of cotical souces in EEG and MEG. The SNR maps wee found to be complementay, suggesting that the highest oveall SNR can be achieved by combined MEG and EEG ecodings. Below, we fist discuss the effect of vaious modeling assumptions, including tissue conductivity values, aveage vs. maximum SNR, numbe of sensos, and senso noise estimates. We then conside the elationship between MEG and EEG and the popeties of the souces in tems of the SNR maps. Modeling Assumptions Ou fowad model used commonly cited values fo tissue conductivities [Hämäläinen et al., 1993]. A ecent study [de Jongh et al., 2005] used electical impedance tomogaphy [Goncalves et al., 2000] to identify the effective conductivities of the thee tissue layes. In MEG, inaccuacy in conductivity values is a mino concen [Hämäläinen and Savas, 1989], wheeas in EEG, inaccuacies in the skull conductivity may significantly contibute to fowad computation eos [Cohen and Cuffin, 1983; de Jongh et al., 2005; Okada et al., 1999b; Takiainen et al., 2003]. Since ou noise estimates wee eithe computed fom the measued data o matched to the expeimental senso vaiances, the effect of the conductivity uncetainties on the noise estimates was mino. Recalculating the noise souce vaiance without EEG, on aveage the diffeence in s s was less than 5% compaed with the calculation that included EEG. This suggests that the uncetainty in the conductivity values had only little effect on the estimated noise souce vaiance. Howeve, if the tue conductivity of the skull is highe o lowe than that used in ou fowad model, ou EEG SNR estimates ae too lage o too small, espectively. We computed the aveage SNR acoss channels [Fuchs et al., 1998], athe than the maximum value of SNR [de Jongh et al., 2005]. The aveage and maximum SNR values will be moe simila fo widespead than fo focal signal pattens. Theefoe, ou use of the aveage SNR is likely to bias the diffeence SNR maps towads lage elative values fo EEG compaed with MEG. It is impotant to take 1081

6 Goldenholz et al. laboatoy. Thee is evidence to suggest that these povide sufficient spatial sampling [Ahonen et al., 1993; Gieve et al., 2004; Lantz et al., 2003]; theefoe, the exact quantity and position of channels is expected to have little effect on the SNR maps. To examine geneal featues of the SNR maps, we used a noise model that only accounted fo the contibutions of unifomly distibuted cotical noise souces, excluding instumentation and envionmental noise. In pactice, howeve, it is pefeable to use session-specific noise esti- Figue 3. Diffeence (D) SNR maps (goup aveage, left hemisphee, plotted on the bain of Subject 2). Top ow: Diffeence (D) maps based on the noise model. Middle ow: D maps based on the noise model thesholded at D 5 0. Red: D > 0 (MEG highe SNR), geen: D < 0 (EEG highe SNR). The sulci, naow cests of gyi, the insula and the medial suface of the cotex all indicate highe SNR in EEG, while othe locations show highe SNR in MEG. Bottom ow: D maps based on ecoded noise thesholded at D 5 0. this into account when compaing the SNR acoss diffeent senso types. Fo example, diffeential ecodings, such as bipola EEG o gadiometic MEG have a moe spatially focal signal patten compaed with the aveage efeence EEG o magnetometic MEG, espectively. We chose the aveage SNR because this measue appeas moe elevant to the application of souce localization techniques which geneally benefit fom seveal sensos exhibiting a high SNR. Pevious studies matched the numbe and locations of EEG electodes and MEG sensos [Fuchs et al., 1998; Liu et al., 2002]. In contast, we compaed a 306-channel MEG system to a 70-channel EEG system, both of which ae cuently used in clinical studies of epileptic patients in ou Figue 4. Effect of the extent of the souce on SNR maps (noise model, goup aveage, left hemisphee, plotted on subject 2 s bain). Top ow: Dipoles. Middle ow: Extended patch souces, adius 5 10 mm. Lowe ow: Extended patch souces, adius 5 16 mm. The colo code indicates at each location the numbe of subjects that have SNR MEG > SNR EEG (i.e. D > 0). Fo example, aeas coloed yellow exhibited D > 0 in all fou subjects at that location. The intemediate colos indicate locations with geate between-subject vaiability. 1082

7 SNR Maps fo MEG and EEG mated fom ecoded data. The diffeence between the esults obtained with the noise model and the ecoded noise may be in pat due to the fact that intinsic backgound bain activity fom focal souces is detected in a geate numbe of sensos in EEG than in MEG; theefoe, egional souces (such as the alpha hythm) have a spatial distibution that is moe simila to that of a widely distibuted noise souce in EEG than it is in MEG. The eye blink, muscle movement, and cadiac atifacts wee not included because they ae of tansient athe than andom natue and can be eithe excluded fom the data analysis o dampened by noise ejection techniques. MEG and EEG Figue 5. MEG and EEG ecoding fom an epileptic patient. Left, above: The EEG signals (standad clinical bipola montage) and potential distibutions at time instants A and B. Left, below: MEG signals in left and ight fontotempoal plana gadiometes and the coesponding field distibutions at times A and B. The spike at time A can be moe easily detected fom the EEG data wheeas the spike at time B is moe pominent in the MEG signals. Right: The locations of the spike souces at times A and B estimated with an equivalent cuent-dipole model ae supeimposed on the diffeence SNR maps (geen: SNR EEG > SNR MEG, ed: SNR MEG > SNR EEG ). Pial cotex maps ae shown as well as expanded SNR maps in the egion of the dipole souce. The SNR maps coectly identify the modality that best epesents the spikes at times A and B espectively. The SNR maps indicated complementay popeties fo MEG and EEG acoss the cotical suface, suggesting that combined ecodings can be beneficial. The SNR of deepe souces, including those in the medial cotex and the insula, was lage in EEG, wheeas the SNR of moe supeficial souces was often lage in MEG. Additionally, locations with souce oientations nomal to the inne skull suface (i.e., adial dipoles), such as the cests of gyi, ae difficult to detect with MEG [Bakley and Baumgatne, 2003; Cohen, 1972; Cuffin and Cohen, 1979; Hämäläinen et al., 1993; Melche and Cohen, 1988; Pataaia et al., 2002]. Howeve, the attenuation of MEG SNR due to oientation is elevant to only 5% of the cotical suface [Hilleband and Banes, 2002]; the majoity of the diffeences can be accounted fo by depth consideations. Ou esults ae consistent with pevious studies whee egional diffeences in MEG and EEG have been obseved. Seveal studies have epoted that MEG is not well suited fo mesial tempoal lobe epilepsy [Baumgatne et al., 2000; de Jongh et al., 2005; Leitjen et al., 2003; Lin et al., 2003; Shigeto et al., 2002]. Convesely, it has been noted that MEG can impove the diagnostic yield fo souces in the lateal aspect of the tempoal lobe [Baumgatne et al., 2000; de Jongh et al., 2005; Leitjen et al., 2003] and in fontal egions [de Jongh et al., 2005; Ossenblok et al., 1999]. It is impotant to note, howeve, that the MEG SNR may be citically affected by the positioning of the senso aay with espect to the head; in paticula, lage diffeences in signals fom fontal souces have been demonstated [Mainkovic et al., 2004]. The SNR maps also help to bette undestand why sometimes cetain epileptic spikes ae detected only in one modality but not the othe [Baumgatne et al., 2000; Iwasaki et al., 2005; Knake et al., 2006; Leitjen et al., 2003; Lin et al., 2003; Yoshinaga et al., 2002; Zijlmans et al., 2002]. In paticula, as the souces that geneate an MEG signal ae, theoetically, only a subset (nonadial, supeficial) of the souces that geneate an EEG signal, it can be counte-intuitive that spikes ae detected in MEG but not in EEG. Howeve, not only the amplitude of the activity, but also the magnitude of noise affects the detectability of spikes in MEG and EEG. Indeed, signals that have been detected in one modality may also be identified in the othe modality, but with low SNR [Yoshinaga et al., 2002, Zijlmans et al., 2002]. Ou patient data also demonstated this. 1083

8 Goldenholz et al. The SNR maps indicated that as the souce extent gew fom a dipole to extended souces of adius 10 mm (aea 5 3cm 2 ) and 16 mm (8 cm 2 ), the numbe of locations with SNR MEG > SNR EEG diminished. This is likely to eflect selective cancellation of signals fom the tangential souce components [Eulitz et al., 1997]. In ou simulations we only vaied the extent of the signal souce, but kept the noise model unchanged. Theefoe, the same tend towads lage elative SNR values fo EEG compaed with MEG would also be found with actual ecoded noise (which is independent of the souce model). Howeve, if extended souce patches wee included in the noise model, this would be expected to compensate fo the changes in the diffeence SNR maps, theeby inceasing the elative SNR of MEG compaed with EEG. In geneal, if one assumes that the souce density is appoximately constant [Lu and Willamson, 1991], a lage patch is expected to esult in lage signals, theeby inceasing the SNR. Ou SNR maps fo extended souces ae consistent with pevious epots of the size of activated ae of cotex undelying detectable EEG and MEG signals. By some estimates, 6 10 cm 2 of cotex must be active fo EEG to detect signals at the scalp [Ebesole and Pedley, 2002; Nunez, 1990]. A combined MEG and intacanial electode study [Baumgatne et al., 2000] stated that a 4 cm 2 patch of cotex was invisible to MEG on the mesial tempoal lobe, while an 8 cm 2 one was visible. In ou study, 3 cm 2 (adius 10 mm) souces had a 10 db lowe SNR than the 8 cm 2 (adius 16 mm) souces in the mesial tempoal lobe. Howeve, the vaiability acoss cotical locations was lage; fo example, the diffeence between 3 cm 2 and the 8 cm 2 souces in the EEG SNR anged fom less than 1 db to moe than 10 db. Theefoe, it is impotant to conside not only the extent but also the anatomical location when assessing the detectability of cotical activation. Clinical Implications The esults of this study have pactical consequences in the clinical evaluation of patients with epilepsy pio to sugey. Because optimized localization esults in bette sugical outcome [Ebesole and Pedley, 2002; Stefan et al., 2003], seizue focus localization should be pefomed with the modality having the highest SNR fo the given position. In addition, it can be employed to test that the head position was appopiate duing a MEG ecoding. The SNR values citically depend on head position [Mainkovic et al., 2004], theefoe any focal souce identified in unexpected cotical egions (such as an occipital focus identified when seaching fo a fontal focus) should be checked fo high enough SNR to be consideed eliable. Similaly, the SNR maps can be used as a moe geneal make of eliability thus souce positions with vey low SNR cannot be consideed as accuately localized as souces with high SNR. When noise is high, spuious signals could esult in geate mis-localization fo egions with low SNR, such as in deep cotical stuctues. It is possible that a method fo a combined MEG and EEG invese model can be achieved by examining the SNR at each senso. It could weigh channels fom simultaneously collected MEG/EEG data on the basis of SNR pe channel. Such a combined invese model may impove the accuacy because estimates will ely on channels moe diectly affected by modeled souces. Patients who undego MEG fo pesugical evaluation outinely obtain simultaneous EEG; theefoe, data fo such a combined invese solution would be easy to implement and could impove localizations. Futhemoe, we believe that knowledge of the SNR values fo vaious cotical egions may be beneficial in the development of a moe statistically igoous souce estimation technique fo epileptic focus evaluation. Such an algoithm would use the eliability index fom the SNR maps as well as the moe taditional measues of eo. The esult would be a moe clinically based test one that could claify the elative impotance of an EEG/MEG souce localization in the context of othe imaging modalities (MRI, positon emission tomogaphy, single photon emission computed tomogaphy, functional MRI, etc) by assigning a pobability of accuacy to souce estimates. CONCLUSIONS The SNR maps wee complementay fo EEG and MEG, displaying egional diffeences among souces within the cotical suface. The noise model based only on unifom backgound bain activity was found to undeestimate the SNR in MEG. With extended souces, the total aea of cotex in which the SNR was highe in EEG than in MEG was lage than that with focal souces. The SNR maps help to bette undestand why epileptic activity is in some cases detected in one modality (EEG o MEG) only. The esults suggest that combing EEG and MEG ecodings is beneficial. 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