Correspondence between EEG-fMRI and EEG dipole localisation of interictal discharges in focal epilepsy

www.elsevier.com/locate/ynimg NeuroImage 30 (2006) 417 – 425

Correspondence between EEG-fMRI and EEG dipole localisation of interictal discharges in focal epilepsy Andrew P. Bagshaw,1 Eliane Kobayashi, Franc¸ois Dubeau, G. Bruce Pike, and Jean Gotman* Montreal Neurological Institute, McGill University, Room 786, 3801 University Street, Montre´al, QC, Canada H3A 2B4 Received 24 March 2005; revised 7 September 2005; accepted 20 September 2005 Available online 2 November 2005 EEG-fMRI and EEG dipole source localisation are two non-invasive imaging methods that can be applied to the study of the haemodynamic and electrical consequences of epileptic discharges. Using them in combination has the potential to allow imaging with the spatial resolution of fMRI and the temporal resolution of EEG. However, although considerable data are available concerning their concordance in studies involving event-related potentials (ERPs), less is known about how well they agree in epilepsy. To this end, 17 patients were selected from a database of 57 who had undergone an EEG-fMRI scanning session followed by a separate EEG session outside of the scanner. Spatiotemporal dipole modelling was compared with the peak and closest EEG-fMRI activations and deactivations. On average, the dipoles were 58.5 mm from the voxel with the highest positive t value and 32.5 mm from the nearest activated voxel. For deactivations, the corresponding values were 60.8 and 34.0 mm. These values are considerably higher than is generally observed with ERPs, probably as a result of the relatively widespread field, which can lead to artificially deep dipoles, and the occurrence of EEG-fMRI responses remote from the presumed focus of the epileptic activity. The results suggest that EEG and MEG inverse solutions for equivalent current dipole approaches should not be strongly constrained by EEG-fMRI results in epilepsy, and that the use of distributed source modelling will be a more appropriate way of combining EEG-fMRI results with source localisation techniques. D 2005 Elsevier Inc. All rights reserved.

Introduction The simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is becoming increasingly widely used in an effort to understand normal and abnormal brain function. It is an attractive technique as it has the potential to combine the high temporal resolution of EEG with the high spatial resolution of fMRI and of constraining the ill-posed * Corresponding author. Fax: +1 514 398 8106. E-mail addresses: [email protected] (A.P. Bagshaw), [email protected] (J. Gotman). 1 Present address: School of Psychology, University of Birmingham, Birmingham B15 2TT, UK. Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2005.09.033

EEG inverse problem with fMRI prior information (Liu et al., 1998; Dale and Halgren, 2001; Toma et al., 2002; Vanni et al., 2004). The pre-operative evaluation of patients with epilepsy is one of the areas where combining EEG and fMRI has considerable clinical relevance. EEG-fMRI has been used extensively in patients with epilepsy in recent years (Krakow et al., 2001; Ja¨ger et al., 2002; Al Asmi et al., 2003; Archer et al., 2003), but the results have rarely been compared with EEG source localisation (Seeck et al., 1998; van der Meij et al., 2001), and only once with dipole localisation (Lemieux et al., 2001). This is despite the fact that EEG dipole localisation is widely used in the study of epilepsy and its accuracy has been assessed by comparison with intracerebral fields (Merlet and Gotman, 1999, 2001), artificial dipoles created by passing a small current through pairs of contacts of implanted electrodes (Krings et al., 1999), PET data (Merlet et al., 1996), clinical and imaging data (Bast et al., 2004; Meckes-Ferber et al., 2004; Pataraia et al., 2005), phantoms (Leahy et al., 1998) and simulations (Kobayashi et al., 2003). Several studies have compared dipole modelling of eventrelated potentials (ERPs) and fMRI, and correspondence on the order of 10 – 15 mm has generally been found (Sanders et al., 1996; Del Gratta et al., 2002; Thees et al., 2003). However, epileptic activity and ERPs are very different EEG phenomena. Epileptic activity is generally of much higher amplitude and generated by a much wider cortical region than ERPs, as well as being of pathological origin, meaning that conclusions drawn from ERP studies concerning the concordance between dipole modelling and fMRI are unlikely to be directly transferable to studies of epilepsy. Much more work is needed to fully assess the correspondence between EEG-fMRI and dipole modelling in the pathological brain. The purpose of the current study was to quantify their concordance in a randomly selected population of patients with focal epilepsy. Materials and methods Patient selection Between June 2001 and May 2003, 57 patients who had an EEG-fMRI examination also underwent an additional EEG record-

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ing session outside of the scanner immediately afterwards. The session lasted for approximately 45 min. This EEG recording will be referred to as the prolonged EEG to distinguish it from the EEG recorded during scanning. Due to its higher signal to noise ratio (SNR) and larger number of electrodes (see below), the prolonged EEG recording was used for dipole modelling. Written informed consent was obtained from all patients and the protocol was approved by the Research Ethics Board of the Montreal Neurological Institute and Hospital. For the current study, in addition to having had an EEG-fMRI session and a prolonged EEG recording, patients were selected according to the following criteria: (1) Focal epilepsy and interictal focal epileptic activity with not more than two distinct types of spikes identified on the EEG recorded in the scanner. Spike types were defined according to their topography and morphology. This constraint was introduced to facilitate the identification of the same spike types in the two EEGs. (2) Consistent spiking pattern between the two EEGs, that is, if spikes of a particular type identified within the scanner could not be identified in the prolonged EEG, the data set was excluded. (3) At least two spikes of a particular type identified on the prolonged EEG and SNR of the averaged spike greater than 4. This was to reduce errors in dipole localisation accuracy as a result of a low SNR (Whittingstall et al., 2003, suggest that the localisation accuracy is essentially independent of SNR, providing SNR >4). Thirty-three patients were selected with not more than two types of spikes within the scanner, leading to a total of 38 spike types. Ten of these were subsequently excluded due to low spike numbers in the prolonged EEG (zero or one spike). One spike type was excluded because it consisted of generalised bursts of activity, although another type from this patient was retained (patient C). Two spike types were excluded due to different topography of the spikes on the two EEGs, and another two for having had less than 44 electrodes used for the prolonged recording (22 and 28, respectively). The latter two were excluded to ensure that additional errors were not introduced into the results because of the effect of different numbers of electrodes on the dipole modelling accuracy (Laarne et al., 2000). Three spike types were excluded due to problems with the automatic detection of spikes (see below) in which the low amplitude spikes could not be distinguished from relatively high amplitude background, and another spike type was rejected due to a low SNR. Finally, one data set was excluded because the quality of the anatomical MRI was not sufficient to allow segmentation and the generation of a realistic boundary element model. With these exclusions, 18 data sets from seventeen patients were analysed (i.e., one patient had two spike types). Note that the patients were selected independently of the results of the EEG-fMRI scanning session and of the dipole modelling. Data acquisition The EEG-fMRI sessions were carried out in a 1.5-T Siemens Sonata scanner (Siemens, Erlangen, Germany) using 21 electrodes and an EMR32 amplifier recording at a sampling rate of 1 kHz (Schwarzer, Munich, Germany). A standard EPI fMRI

sequence was used (voxel dimensions 5  5  5 mm, 25 slices, 64  64 matrix, TE = 50 ms, TR = 3 s, flip angle 90-) and a 3D T1-weighted anatomical scan was also acquired prior to fMRI recording (1 mm slice thickness, 256  256 matrix, TE = 9.2 ms, TR = 22 ms, flip angle 30-). The fMRI data were acquired in runs of 120 images taking approximately 6 min each, followed by a short pause. The artefact induced on the EEG recording by the gradient switching during fMRI scanning was removed using FEMR software (Schwarzer; Hoffmann et al., 2000). The patient’s head was immobilised using a vacuum-bag filled with polystyrene spheres (S&S X-Ray products, New York, USA). The scanning session lasted for approximately 2 h in total, with between 5 and 12 runs of fMRI data acquired for each patient (see Table 1). Following the EEG-fMRI scanning session, the patient was taken directly from the scanner to the clinical EEG department. Extra electrodes were added according to the 10 – 10 standard leading to a total of 44 electrodes (the standard 19 of the 10/20 system, F9, T9, P9, FC1, FC3, CP1, CP5, PO3, PO7, and symmetrically on the right, plus AFz, FCz, CPz, POz, Oz, one EKG and the reference). Approximately 45 min of EEG data were acquired at a sampling rate of 200 Hz with the patient in a relaxed position (Harmonie, Stellate, Montreal, Canada). EEG-fMRI statistical analysis The EEG-fMRI data were motion corrected and smoothed (6 mm full-width at half maximum; FWHM). The first three frames of each run were not included in the analysis to ensure that the magnetisation was in a steady state. Differences in the slice acquisition time were corrected for. Temporal autocorrelations were accounted for by fitting an AR model of order 1 (Worsley et al., 2002), and low frequency drifts in the signal were modelled with a third order polynomial fitted to each run. Following removal of the gradient artefact, epileptic spikes in the EEG recorded during fMRI scanning were identified by an experienced neurophysiologist. They were separated into distinct types according to their spatial distribution and morphology, and an EEG-fMRI data set was defined for each type of discharge. An event-related fMRI analysis was performed using the timing of the spikes with the methods and software of Worlsey et al. (2002). Each data set was analysed with four monophasic, single gamma functions peaking after 3, 5, 7 and 9 s with an FWHM of 5.2 s (Bagshaw et al., 2005). This allowed for some variation in the latency of the response while retaining information about its expected shape. Composite statistical maps were created by taking the maximum value from the four analyses at each voxel. Clusters of five contiguous voxels with a t value above 3.1 were considered activated, and similarly for deactivations ( P < 0.05 corrected for the use of multiple haemodynamic response functions, Cao, 1999). Spike detection and averaging Epileptic spikes were identified semi-automatically using BESA 2000 (MEGIS Software GmbH, Germany), following the method of Bast et al. (2004). The EEG was bandpass filtered between 1.6 and 35 Hz and then manually inspected to identify a typical spike. An epoch from the start of the spike until the peak of the slow wave was used as a template for a pattern matching algorithm with a correlation threshold of 0.75 (i.e., all segments having a correlation

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Table 1 Details of the patients’ epilepsy syndrome, anatomical MRI, spikes, number of runs of fMRI data collected and associated number of spikes, number of spikes constituting the averaged spike used for EEG dipole analysis, number of dipoles fitted and modifications to the BEM model Data set

Epilepsy syndromea

Anatomical MRI

Spike distribution

A

CPLE

R T (regional)

9

B

TOLE

L T (regional)

C

FLE (SMA)

D E F

TLE TOLE Insular Epilepsy

R hemispheric atrophy with posterior periventricular leucomalacia Bi O periventricular heterotopia + LT resection L porencephalic cyst (L MCA) Anterior L T gliosis Bi P-O atrophy R T resection

G

PLE

L SMA resection

Hb Ib J

TOLE TOLE TLE

K L M

TLE TLE FLE

Normal Normal Multiple cavernous angiomas R amygdala DNET R HA Normal

N O P1 P2 Q

PLE TLE TLE TLE CPLE

L P FCD L T atrophy Bi perisylvian PMG Bi perisylvian PMG R perisylvian PMG + RT resection

No. runs of fMRI data

No. spikes during EEG-fMRI

No. spikes in dipole average

No. dipoles fitted

BEM model

18

23

1

Standard

10

98

13

2

L F (focal)

12

38

10

2

L T resection, L TP skull hole Lesion (cyst)

L T (focal) Bi O (Bi regional) R T (focal)

9 7 10

4 91 12

6 73 4

2 2 2

L PO (focal)

6

9

23

1

R O (focal) R TO (focal) L T (focal)

10 10 11

14 17 594

34 16 13

1 1 3

L T (regional) R T (focal) Bi FT max L (Bi regional) L CP (regional) L T (focal) R T (focal) L T (focal) R FC (regional)

5 10 10

2 5 17

6 10 24

2 2 2

8 9 7 7 9

56 84 2 14 8

11 97 3 3 66

1 1 2 2 2

Standard Standard R T resection, R T and R TP skull holes L F resection, L P skull hole Standard Standard L T and L TP skull holesc Standard Standard Standard Standard Standard Standard Standard R T resection, R F and R T skull holes

A Fstandard_ BEM model is one with three surfaces with no holes or lesions/resections. Abbreviations: L = left; R = right; Bi = bilateral; T = temporal; F = frontal; O = occipital; P = parietal; C = central; MCA = middle cerebral artery; SMA = supplementary motor area; DNET = dysembryoplastic neuroepithelial tumour; HA = hippocampal atrophy; FCD = focal cortical dysplasia; PMG = polymicrogyria. a FLE is frontal lobe epilepsy etc., according to the abbreviations given above. b Two different sessions in the same patient. c A small cavity was not modelled in this patient.

greater than 0.75 with the template were identified). The results of this search were visually inspected and individual events removed if there were other events or problems with the baseline in the 8 s before the spike. This was because the dipole modelling (see below) used a period of 3 – 8 s before the spike to estimate the noise in the signal. The remaining events were averaged and the average used as a second template with a correlation threshold of 0.85. Finally, events were again rejected based on the background EEG before the spike and the events which remained were used to form the averaged spike. In patient J this semi-automated technique lead to a considerable difference between the spike rate during scanning and the apparent spike rate in the prolonged EEG. This was a result of very frequent spiking and many events being discarded from the prolonged EEG because of the 8 s that were required between spikes in order to allow the noise to be characterised. Table 1 gives details of the number of spikes averaged and the spike distribution. As well as the topographic location of the spikes, the distribution is divided into two groups: focal and regional. For example, focal spikes were restricted to very few electrodes placed over the same quadrant, such as T6 and O2, while those in the regional group were more widespread.

BEM model construction and dipole modelling Realistic head models were created using the boundary element method (BEM) as implemented in Curry V4.5 (Neuroscan, Hamburg, Germany). For each patient the anatomical MRI was segmented automatically with manual modification in the case of poor segmentation. The positions of the 21 electrodes used for the EEG-fMRI scanning session were visible on the segmented skin surface and were marked accordingly. The positions of the remaining electrodes that were added for the prolonged recording were estimated using the original electrodes as landmarks. BEM surfaces were created for the scalp, skull and brain with mesh densities of 10, 9 and 7 mm, leading to a model with approximately 4000 nodes in total. In the case of patients who had previously undergone surgery, additional modelling of the resection and skull holes was performed (Be´nar and Gotman, 2002). The resection was manually identified and an additional surface (6 mm mesh density) included in the model. To prevent numerical instabilities, a gap of approximately 5 mm was left between the resection and brain surfaces. At times this resulted in the resection being modelled as slightly smaller than in reality, and in patient J a

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Fig. 1. The dipole fitting protocol.

small cavity was not modelled. A similar process was followed for a patient with a large cyst (patient C). In the case of skull holes, the skull and brain surfaces were combined to form one surface, with holes in the appropriate positions. A mesh density of 9 mm was used for this surface, with local refinement around the hole leading to a region with 2-mm mesh density. Conductivities were set to 1 S/m for CSF (resections/ cyst), 0.33 S/m for brain and scalp and 0.0165 S/m for skull. This latter value corresponds to a ratio of 1:20 for the conductivities of skull and brain (Oostendorp et al., 2000). The number of dipoles to be fitted was determined by performing a Principal Components Analysis (PCA) on the averaged spike from its onset to the peak of the slow wave (Bast

et al., 2004), with as many dipoles fitted as PCA components were required to explain more than 95% of the variance of the signal. A spatiotemporal (ST) approach was used, as demonstrated in Fig. 1 (Merlet and Gotman, 1999, 2001). In the case of a single component being required to explain the data, a single fixed dipole was fitted from the spike onset to the peak of the slow wave. In the case of two dipoles, the first was fitted over the first ascending phase of the spike, and the second from spike onset to the peak of the slow wave. If a third source was required, it too was fitted from spike onset to the peak of the slow wave. As a separate analysis, instantaneous dipoles were fitted at the peak of the negative and positive deviations of the averaged spike. In each case, the same number of dipoles were fitted as for the ST

Fig. 2. Results for patient A (a) fMRI activation, (b) dipole location and (c) both.

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Fig. 3. Results for patient E showing the fMRI deactivation and the first dipole.

approach. Table 1 gives details of the number of dipoles fitted, and of the BEM. Comparison of EEG-fMRI and dipole modelling In order to allow the comparison of the EEG-fMRI and dipole modelling results, the fMRI statistical map was resampled to the spatial resolution of the anatomical MRI scan, and the dipole coordinates were transformed to the space of the anatomical scan. Distances between the dipoles and the peak positive and negative fMRI voxels were calculated using software written in Matlab (The MathWorks Inc., MA, USA). Distances between the dipoles and the nearest activated and deactivated voxels were also calculated. The results were visualised using Anatomist (www.brainvisa.info). In Figs. 2 – 6 activations are represented on a yellow colour scale and deactivations on a blue colour scale. Results The fMRI statistical maps were not examined until all of the dipole modelling had been completed to avoid biasing the dipole modelling process. For all but one spike type, the residual variance was less than 10% (see Table 2). Subsequently, it was found that four patients (data sets F, H, I and L) did not have any significant fMRI activations or deactivations. All of these patients had very focal spikes. In addition, patient M was excluded because the statistical map contained very widespread regions of activation and deactivation that may have been artifactual and certainly rendered a comparison with the location of the dipoles largely meaningless. Comparisons between the fMRI results and the dipole modelling were thus made for thirteen data sets from twelve patients. The distances between the ST dipoles and the peak and closest fMRI responses are given in Table 2. There were no systematic differences between the results for the ST dipole fitting approach and those for instantaneous dipoles fitted at the negative and positive peaks of the averaged spike, so for brevity only the ST dipoles will be discussed. The mean distance between the dipoles and the fMRI voxel with the highest positive t value was 58.5 mm,

421

and 60.8 mm for the voxel with the highest negative t value. The distance between the closest positively and negatively activated voxels and the dipoles was 32.5 mm and 34.0 mm, respectively. Ignoring the polarity of the fMRI response, dipoles were on average 23.0 mm from the closest voxel with a significant response. There were no significant differences between the distances measured when the EEG data were fitted with a single dipole and multiple dipoles ( P > 0.1, two-tailed t test). When more than one dipole was fitted there was no significant difference in the measured distances for the first dipole, fitted to the first ascending phase of the spike, and the second and third dipoles, which included the slow wave portion of the spike ( P > 0.5, two-tailed t test). Figs. 2 – 6 show examples of the coregistration of fMRI with the primary dipole. In all cases the results of the ST dipole modelling are shown. A short section of EEG is shown to demonstrate the distribution of the averaged spike. This EEG is in average reference. In Fig. 2 are the results for patient A. There was lobar agreement between the dipole and the fMRI activation. The dipole was in the inferior, anterior temporal lobe, while the fMRI Table 2 Distances between the dipoles and the fMRI responses, and the residual variance of the dipole fitting Data set

Residual Dipole Distance to peak Distance to closest variance fMRI response (mm) activated voxel (mm) (%) Positive Negative Positive Negative

A B

2.6 7.8

C

3.5

D

6.7

E

5.3

F G H I J

5.3 8.8 7.9 7.4 16.9

K

2.8

L M

3.4 6.0

N O P1

2.1 1.9 8.6

P2

8.6

Q

1.5

Average

1 1 2 1 2 1 2 1 2 1 1 1 1 1 2 3 1 2 1 1 2 1 1 1 2 1 2 1 2

50.0 40.5 61.8 45.0 56.2 43.7 54.7 96.4 117.7 x 50.7 x x 48.2 73.0 77.5 35.7 20.0 x y y 37.0 50.0 75.0 54.6 94.6 80.6 45.0 36.5 58.5

70.2 50.3 34.0 70.6 82.1 37.3 33.8 42.5 94.0 x x x x x x x 78.7 75.2 x y y 70.4 x x x x x 55.2 56.3 60.8

24.0 16.5 35.0 0.3 5.0 5.1 15.3 88.5 108.4 x 44.1 x x 5.9 17.6 14.7 27.5 11.6 x y y 23.7 36.6 56.7 8.6 81.8 65.9 28.8 25.4 32.5

55.3 18.0 9.0 19.6 24.5 34.7 31.4 2.1 1.8 x x x x x x x 72.9 67.2 x y y 61.0 x x x x x 38.2 40.7 34.0

The distances to the peak activated and deactivated voxels are given, as well as those to the closest activated and deactivated voxels. An Fx_ indicates that there was no significant fMRI response. A Fy_ indicates that the data set was excluded (see text).

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Fig. 4. Results for patient J showing the fMRI activation and the first dipole in (a) sagittal and (b) axial slices.

activation was considerably more superior and posterior, near the temporoparietal junction. Not shown is a small deactivation in the superior parietal lobe. Fig. 3 shows the results for patient E. In this case the primary fMRI response was a large bilateral occipital deactivation with right sided predominance. The predominant dipole was placed in the mesial aspect of the left temporo-occipital junction. Although the dipole was close to a region of deactivation it would seem likely that it was artificially deeper than the region of cortex generating the spikes as a result of the bilaterality of the spike field. A small region of fMRI activation was also found in the left frontal region. The second dipole was located close to the junction of the left occipital lobe and the cerebellum. Fig. 4 shows the first dipole and the main region of fMRI activation in patient J. Although the voxel with the highest t value was at the left temporal pole, the activation also stretched more posteriorly and very close to the first dipole. The majority of the activation was restricted to the left temporal lobe, although there was also activation in the contralateral temporal lobe. No deactivation was observed. The second dipole was very deep in the left mesial posterior temporal lobe. The third dipole had a very low amplitude and was located in the cingulate gyrus.

Fig. 5 demonstrates the disagreement between dipole modelling and fMRI that was observed in patient O. The dipole was left temporal, in agreement with the EEG field, but the fMRI activation was extratemporal. No fMRI activation was found in the left temporal lobe. Activation was also observed in the contralateral hemisphere. No deactivation was observed. Finally, patient Q is shown in Fig. 6. The EEG field for this patient was widespread, and the maximum of the fMRI activation was in the left parietal lobe. Some activation was also observed in the contralateral parietal lobe, and some deactivation in the right frontal region. Two dipoles were fitted and both were placed subcortically. As with patient E (Fig. 3) the dipoles were located in artificially deep locations as a result of the widespread spike field.

Discussion The combination of EEG with fMRI is an attractive tool for investigating normal and pathological brain function, and as it becomes more straightforward to record good quality EEG from a large number of electrodes it is likely to be increasingly widely used. Although there are clear conceptual difficulties with reducing

Fig. 5. Results for patient O (a) coronal cut showing the fMRI activation and the dipole location and (b) sagittal cut.

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423

Fig. 6. Results for patient Q showing the fMRI activation and the first dipole in (a) sagittal and (b) coronal slices.

the measured electric field to a few representative points, dipole modelling remains the most commonly applied source reconstruction technique. With this in mind, the current study investigated the correlation between EEG dipole modelling and EEG-fMRI in a group of patients with focal epilepsy. Patients were selected without prior knowledge of the fMRI results in an attempt to minimise the bias towards one or other of the localisation techniques and to give an idea of what might be expected if EEG dipole localisation and EEG-fMRI were used routinely in a standard patient population. On average, dipoles were 32.5 mm from the nearest activated and 34.0 mm from the nearest deactivated fMRI voxel, with the dipole as likely to correspond to the region of deactivation as the region of activation. This was independent of the type of dipole modelling (spatiotemporal and instantaneous dipoles fitted at the negative and positive peaks of the averaged spike). This result is in general agreement with that of Lemieux et al. (2001), who scanned six patients with epilepsy using 12 scalp electrodes and compared the resulting spike-triggered EEG-fMRI responses with EEG dipole source analysis from a separate session using 64 electrodes. Over the six patients, they measured mean distances between dipoles and the centre of the nearest fMRI response of 4.2 cm (spatiotemporal approach), 3.5 cm (moving dipole fitted to the first negative peak of the spike) and 2.2 cm (moving dipole fitted to the later positive peak of the spike). The effect of the dipole modelling approach was not observed in the current study. Since they measure fundamentally different properties of brain function, complete concordance between EEG and fMRI is not expected (Nunez and Silberstein, 2000), even when the electrical activity is measured directly from the cortex (Disbrow et al., 2000; Krings et al., 2001). However, the distances between dipoles and EEG-fMRI activations measured in the current study and that of Lemieux et al. (2001) are considerably larger than is generally observed when comparing ERP dipole modelling and fMRI. Several studies have found concordance on the order of 10 – 15 mm, often using MEG in the somatosensory region (Sanders et al., 1996; Kober et al., 2001; Del Gratta et al., 2002; Schulz et al., 2004), but also with EEG and in other regions (Ahlfors et al., 1999; Thees et al., 2003). There are several possible explanations for this. From a technical point of view, since many studies used high density MEG with up to 150 channels, the MEG dipole modelling may be more accurate than EEG as a result of the number of recording

channels and the added complexity of the forward model that is necessary with EEG dipole localisation. In support of this idea, a combined EEG/MEG study in mesial temporal lobe epilepsy found that EEG equivalent dipoles tended to be localised deeper than their MEG counterparts, although the MEG results did not show consistently better agreement with electrocorticographic recordings (Leijten et al., 2003). While this may be an issue, at least one study using 32 channel EEG found concordance between dipoles and fMRI on the order of 1 cm in the somatosensory region (Thees et al., 2003). A more likely explanation for the rather poor concordance between EEG dipoles and EEG-fMRI is the relative complexity and widespread distribution of the field associated with epileptic activity compared with ERPs, which will lead to a dipole that is located considerably deeper than the region responsible for its generation (Nunez and Silberstein, 2000), as is evident even for the focal spikes of patient J (Fig. 4) that result in a mesial temporal dipole. Added to this, it seems to be more likely that a robust fMRI response will be detected in patients with widespread EEG abnormalities. This is emphasised by the fact that the four patients without any fMRI responses all had very focal spikes (see Table 1), suggesting that often very focal spikes do not lead to significant fMRI responses at all. The contrary of this has been seen in a previous study of patients with generalised epilepsy with bursts of spike and wave discharges, in whom fourteen out of fifteen had significant responses (Aghakhani et al., 2004), a much higher percentage than is generally seen in groups of focal epilepsy patients (Al Asmi et al., 2003; Bagshaw et al., 2004). As a consequence, it is often the case that patients in whom fMRI can be reliably compared with EEG dipole modelling are just those in whom the use of dipole modelling is most inappropriate. Extreme examples are patient E (Fig. 3) whose bilateral occipital spikes resulted in a predominant midline dipole, and patient Q (Fig. 6), whose spikes had a very widespread field and whose dipoles were located subcortically. This highlights the difficulty in comparing dipoles with fMRI, particularly in epilepsy. A dipole is a mathematical device to represent the measured electromagnetic field and as such does not have a neurophysiological basis. Often the combination of the dipole location and its orientation is considered to be a more reliable indicator of the cortical region generating the discharge than the location alone (Ebersole, 1997; Pataraia et al., 2005). In particular, it is striking that in the current study there was essentially no correlation between the peak fMRI

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responses and the dipoles (mean distances of 58.5 mm for activations and 60.8 mm for deactivations). Of course, not all of this discrepancy can be attributed to the inadequacies of dipole modelling. In the comparison of EEGfMRI and EEG dipole modelling, both methods can give results that are not well correlated with the known clinical information, and in the absence of a gold standard to identify the epileptogenic zone it is not possible to give a definite answer about which method is more accurate. BOLD fMRI has an inherent tendency to detect regions of activation that are at some distance from the site of neuronal activation due to its sensitivity to changes in draining veins (Lai et al., 1993). In addition, in patients with epilepsy, significant fMRI activations and deactivations can sometimes be seen in regions that appear to be far removed from the assumed focus of the epileptic activity (Al Asmi et al., 2003; Bagshaw et al., 2004). This is demonstrated in Fig. 5, where focal temporal spikes resulted in only extratemporal fMRI responses but a temporal dipole. The significance of the fMRI response in cases like this remains unclear, but such results appear to be relatively common in temporal lobe epilepsy (Kobayashi et al., in press-a). It was not the purpose of the current study to attempt to provide a detailed examination of the link between the dipole and fMRI response locations and the clinical history of the patients. Clearly, much more work is needed to understand the physiological and clinical significance of BOLD responses in patients with epilepsy, and their relationship with dipole source localisation models. It is likely that patients who have undergone long-term EEG telemetry, intracranial EEG or a seizure-free outcome of surgery will be required to be the subjects of such studies. However, it is important to emphasise that EEG-fMRI and EEG dipole modelling provide different localising results, starting from the same event. The discrepancy between the results observed in the current study is thus an essential problem that must be resolved, almost independently of the agreement with other methods of investigation. In many patients regions of deactivation were observed, corresponding to consistent decreases in the BOLD signal following the spikes. It is perhaps surprising that the excessive neuronal activity that produces the spikes should result in BOLD reductions, but this is often seen in patients with epilepsy, as summarised recently by Kobayashi et al. (in press-b). In that study, deactivations were usually not focal, were generally observed in patients who also showed activations (with no overlap of the spatial distribution of both types of response) and tended to occur less frequently in the anatomical regions thought to be related to the scalp EEG field than activations. Another recent study used combined BOLD and arterial spin-labelling perfusion measurements to investigate the origin of deactivations as a result of generalised spike and wave discharges (Stefanovic et al., 2005), and found that the BOLD reductions were accompanied by perfusion reductions. It is interesting that in the current study the deactivations were not less correlated with the dipoles than the activations, perhaps indicating that they are both important in the generation of the epileptic discharges, and certainly suggesting that both types of response should be considered when interpreting the EEG-fMRI statistical map. In conclusion, the results demonstrate considerably worse agreement between fMRI and EEG dipole modelling than is generally observed in ERP studies, probably as a result of the relatively widespread field associated with interictal discharges, which can lead to artificially deep dipoles, and the occurrence of

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