Clinical Neurophysiology xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Clinical Neurophysiology

journal homepage: www.elsevier .com/locate /c l inph

Epileptic focus localization based on resting state interictal MEGrecordings is feasible irrespective of the presence or absence of spikes

http://dx.doi.org/10.1016/j.clinph.2014.07.0141388-2457/� 2014 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.

⇑ Corresponding author. Address: Cleveland Clinic, Epilepsy Center, NeurologicalInstitute, 9500 Euclid Ave., Cleveland, OH 44195, USA.

E-mail address: alexopa@ccf.org (A.V. Alexopoulos).

Please cite this article in press as: Krishnan B et al. Epileptic focus localization based on resting state interictal MEG recordings is feasible irrespectivpresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.07.014

B. Krishnan a, I. Vlachos b, Z.I. Wang a, J. Mosher a, I. Najm a, R. Burgess a, L. Iasemidis b, A.V. Alexopoulos a,⇑a Cleveland Clinic Epilepsy Center, Cleveland, OH, USAb Biomedical Engineering, Louisiana Tech University, LA, USA

a r t i c l e i n f o

Article history:Accepted 18 July 2014Available online xxxx

Keywords:MEGInformation flowEpilepsyInterictal periodEpileptogenic focus localizationResting state connectivity

h i g h l i g h t s

� For the first time a connectivity-based analysis of MEG data is tested for epileptogenic focus localiza-tion in patients with neocortical epilepsy.

� MEG recordings from five patients with medically-intractable focal epilepsy undergoing presurgicalevaluation for resection of candidate epileptic focus were analyzed.

� Proposed algorithm can localize the epileptogenic focus accurately even in patients with normal MRI(MRI-negative) or patients without interictal abnormalities on scalp EEG (EEG-negative) or MEG(MEG-negative).

a b s t r a c t

Objective: To investigate whether epileptogenic focus localization is possible based on resting state con-nectivity analysis of magnetoencephalographic (MEG) data.Methods: A multivariate autoregressive (MVAR) model was constructed using the sensor space data andwas projected to the source space using lead field and inverse matrix. The generalized partial directedcoherence was estimated from the MVAR model in the source space. The dipole with the maximum infor-mation inflow was hypothesized to be within the epileptogenic focus.Results: Applying the focus localization algorithm (FLA) to the interictal MEG recordings from fivepatients with neocortical epilepsy, who underwent presurgical evaluation for the identification of epilep-togenic focus, we were able to correctly localize the focus, on the basis of maximum interictal informa-tion inflow in the presence or absence of interictal epileptic spikes in the data, with three out of fivepatients undergoing resective surgery and being seizure free since.Conclusion: Our preliminary results suggest that accurate localization of the epileptogenic focus may beaccomplished using noninvasive spontaneous ‘‘resting-state’’ recordings of relatively brief duration andwithout the need to capture definite interictal and/or ictal abnormalities.Significance: Epileptogenic focus localization is possible through connectivity analysis of resting stateMEG data irrespective of the presence/absence of spikes.

� 2014 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophy-siology.

1. Introduction

Resection of epileptogenic tissue has emerged as one of themost promising therapies in controlling seizures in patients withdrug resistant focal epilepsy. Successful surgical outcome occurs

in patients in whom the epileptogenic focus can be identified witha high degree of confidence. Magnetoencephalography (MEG) is anattractive tool for non-invasive localization of the epileptic focus.As an electromagnetic modality, MEG has inherently higher spatialresolution than standard scalp EEG, higher sensitivity to fissuralsources, a ‘‘broader’’ view of whole-head activities, and less signaldistortion from intervening tissues, such as skull and dura (Cohenand Cuffin, 1983; Kakisaka et al., 2012a). Multiple studies haveindicated that MEG aids presurgical evaluation of patients with

e of the

2 B. Krishnan et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

focal epilepsy (Knowlton et al., 1997; Stefan et al., 2000; Knakeet al., 2006; Schneider et al., 2012). However, the absence of inter-ictal abnormalities in routine short-term (typically 60 min) MEGstudies reduces the yield of MEG, since focus localization reliesdirectly on the identification of epileptiform transients in therecorded data. While prolonging MEG recording time is technicallypossible (Zhdanov et al., 2013), longer recording times are notpractical. Moreover, analysis of epileptiform transients on MEGcan be subjective and time-consuming, as it requires detailedvisual inspection of recordings from hundreds of channels by atrained epileptologist/neurophysiologist.

From a network theory framework, the brain can be visualizedas a system of interconnected neuronal pools with features of com-plex networks, such as small world topology and modularity (Wattsand Strogatz, 1998; Achard et al., 2006; Bassett and Bullmore,2006). An improved understanding of the properties of the epilep-togenic network could provide important clues to the process ofepileptogenesis. Investigation of connectivity in the MEG sourcespace forms one of the major cornerstones of the ongoing BrainConnectome Project (Insel et al., 2013; Larson-Prior et al., 2013).We propose a novel strategy for noninvasive localization of theepileptic focus based on analysis of resting state (in this case‘‘resting state’’ means that no epileptiform discharges are neces-sary) directional connectivity of the epileptic brain using advancedsignal processing (referred to as focus localization algorithm, FLA)of MEG data. This approach is based on the premise that the epilep-tic focus has abnormal information inflow from neighboring neuralstructures during the resting state (Krishnan et al., 2013; Vlachoset al., 2013).

As part of our continuous effort to clinically validate the FLA, wechose to apply it to MEG data obtained from a spectrum of patientswith drug resistant focal epilepsy. In this study we compare thepreliminary results of FLA with clinical localization based onMEG single equivalent dipole modeling, ICEEG onset (when avail-able), area of resection and seizure outcome.

2. Methods

2.1. Patient selection

MEG recordings from five representative patients undergoingMEG as part of their pre-surgical evaluation were analyzed in thisproof-of-concept study. Each patient represented a special case interms of the success/failure of the utilized modality for focus local-ization (see Table 1).

2.2. MEG recording

Sixty minutes of MEG was recorded with a 306-channel whole-head MEG system (Elekta, Sweden) with a sampling frequency of1000 Hz and acquisition filtering from 0.1 Hz to 333 Hz. All MEGdata were post-processed using a temporally-extended signalspace separation (tSSS) algorithm (Taulu et al., 2004; Taulu andHari, 2009), which also corrects for minor head movements in

Table 1Patient characteristics.

Patient info. Clinical localization MRI S

P1 (54 M) Right prefrontal cortex L CP2 (34 M) Right frontal operculum N CP3 (48 M) Left inferior frontal sulcus L CP4 (42 F) Right superior & middle frontal gyri N DP5 (64 M) right peri-rolandic Cortex (epilepsia partialis continua) L I

L, lesional; N, normal; C, concordant, D, discordant; I, indeterminate; FCD, focal cortical

Please cite this article in press as: Krishnan B et al. Epileptic focus localization bapresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1

the MEG array (Kakisaka et al., 2012b; Nenonen et al., 2012). Aspart of the patients’ clinical evaluation, source localization analysiswith standard single equivalent current dipole (SECD) wasperformed using Neuromag’s XFIT software (Elekta, Stockholm,Sweden). The location, orientation, and strength of the dipolesources that best fit the measured magnetic fields were calculatedbased on the SECD model (Stefan et al., 2003; Pataraia et al., 2004).We employ a standard approach in SECD analysis in all patientswith medically intractable epilepsy undergoing clinical MEGrecordings in our MEG laboratory (Alkawadri et al., 2013). SECDanalysis was performed on data segments containing epileptiformdischarges with dipole modeling just before or at the peak of theglobal field power of each interictal activity (one or several clustersof dipoles, one dipole per spike). No ictal episodes were recordedfor the five patients. For FLA analysis, the processed MEG data werefurther filtered using a Butterworth filter (a zero phase digital filterwas realized) with passband edges at 1 Hz and 30 Hz and down-sampled to 200 Hz. Since FLA analysis does not depend on the pres-ence or absence of epileptiform discharges, segments containinginterictal abnormalities were not removed. Only the tSSS algorithmwas used for preprocessing the MEG data and correct for artifacts.Performance of tSSS algorithm in removing strong interferencecaused by external and nearby sources has been previouslydiscussed in Taulu and Hari (2009), Wang et al. (2013)).

2.3. Focus localization algorithm

The main part of the algorithm is the estimation of the multivar-iate autoregressive (MVAR) model representation of the recordeddata and the quantification of the connectivity in the frequencydomain on the basis of the MVAR model Details of the algorithmhave been previously published (Krishnan et al., 2013; Vlachoset al., 2013), are described below and illustrated in Fig. 1.

The proximity of MEG sensors introduces co-linearity in thesensor time series, which might result in inaccurate estimation ofthe MVAR model. To counteract this effect, we first apply principalcomponent analysis (PCA) on the sensor data and select the PCAcomponents that explain 99% of the data variance (energy). Inthe second step we estimate the MVAR model representation oforder p (equal to 7) on the PCA transformed data. Model order(p) of 7 was selected for the MVAR model based on our priorstudies on brain dynamics in epilepsy (Iasemidis et al., 1990,2000, 2011). A model order of ‘p’ signifies that p past samples ofXb will be used to predict the current value of Xb (see Fig. 1). TheMVAR model constructed in the PCA space is then projected tothe source space using the lead field and inverse matrix. The ratio-nale for this approach is supported by published literature(Michalareas et al., 2013). One thousand dipoles uniformly distrib-uted over the cortex were used to create the forward and inversemodel. The overlapping sphere approach (Huang et al., 1999) isused to estimate the forward model and weighted minimum normestimate (wMNE) (Baillet et al., 2001) to create the imaging kernel.Empty room recording was used to estimate the noise covariancematrix. To determine directional connectivity between theassumed dipoles for a 10 s epoch of the recorded MEG data

ECD localization FLA localization Seizure freedom (months) Pathology

C 57 FCDC 51 FCDC N/A N/AC 26 FCDC N/A N/A

dysplasia.

sed on resting state interictal MEG recordings is feasible irrespective of the016/j.clinph.2014.07.014

Fig. 1. Steps of the directional connectivity analysis.

B. Krishnan et al. / Clinical Neurophysiology xxx (2014) xxx–xxx 3

(denoted epoch s) we estimate the generalized partial directedcoherence (GPDC) (Baccala et al., 2007) between the dipoles iand j using the projected MVAR model in the source space as:

Gi;jðf; sÞ ¼jBijðfÞj=riiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPN

k¼1jBkjðfÞj2=r2kk

q ð1Þ

where rii are obtained from the covariance matrix R ¼ ½rij�i;j¼1...n ofthe noise process e(t) corresponding to the MVAR model for thegiven epoch, Bijðf Þ is the ði; jÞth element of the matrixBðf Þ ¼ I�

Pps¼1BðsÞe�i2pfs, BðsÞ are the projected coefficient matri-

ces of the MVAR model and I is the 1000� 1000 identity matrix.(Refer to Fig. 1 for definitions of e(t) and BðsÞ).

MEG data segments with interictal abnormalities were notremoved during FLA processing. Typical duration of MEG record-ings is T = 60 min. GPDC is the normalized version of Partial Direc-ted Coherence (Baccalá and Sameshima, 2001) and has foundapplications in the study of brain dynamics in other than epilepsydisorders in the past (Astolfi et al., 2006, 2007; Silfverhuth et al.,2011). The average ð�Gi;jÞ of the 10-s GPDC between a pair of dipolesi and j over the entire duration of MEG recording and across fre-quencies 0.1–30 Hz is estimated as

�Gi;j ¼< Gi;jðf ; sÞ>f ;s ð2Þ

where < �>f;s denotes the average across all possible epochs andfrequency values. �Gi;j denotes the amount of information flowingfrom dipole j to dipole i. For each dipole i, we estimate the averagelocal inflow (Ci), from its neighbor dipoles located within the 3-dimensional sphere with center the dipole i and radius r (set to5.5 cm in our analysis):

Ci ¼1

Ni;r

Xj0

�Gi;j; j : dði; j0Þ 6 r ð3Þ

where dði; j0Þ is the Euclidean distance between dipoles i; j0 and Ni;r

is the number of neighbor to dipole i within the sphere of radius r.The values of Ni;r ranged from 100 to 200.

In this realization, to quantify the presence of abnormal connec-tivity, we compared the average inflow Ci of dipole i with the oneat the corresponding dipole on the contralateral hemisphere. Thiswas done by performing an unpaired t-test between the Ci valuesof the 60 closest neighbors of dipole i (including dipole i) and itscounterpart in the contralateral hemisphere. The Ci values ofdipoles i with statistically significant inflow (p < 0.01) comparedto the ones of dipoles in homologous regions of source spacewithin the contralateral hemisphere were further considered in

Please cite this article in press as: Krishnan B et al. Epileptic focus localization bapresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1

the analysis, while the remaining (non-significant) C values wereset to zero. The algorithm was implemented in Matlab 2012b(MathWorks, Natick, MA). The head model and inverse sourcemodel were constructed for each patient using the freely availableMEG toolbox Brainstorm (Tadel et al., 2011).

2.4. Concordance analysis

For the patients who underwent surgery, FLA results weredeemed to be concordant if FLA localized region was within thearea of resection. For the two patients who did not undergo surgi-cal removal of epileptogenic tissue, we compared the focus loca-tion results from intracranial recordings (Patient 3) and MRIlesion (Patient 5) with the localized region from FLA. Sublobar con-cordance (Schneider et al., 2012) was achieved in both these cases.

3. Results

3.1. Case 1: MRI positive, SECD and FLA concordant; right hemisphere

A 54 year-old right-handed male with a history of medicallyintractable focal motor seizures starting at the age of 11 years.He presented with daily seizures that were preceded by a ‘‘pullingsensation’’ over the left arm followed by asymmetric tonic stiffen-ing of both arms with preserved awareness, and rare secondarilygeneralized seizures. Neurological examination was normal.

3.1.1. Presurgical evaluationDuring a period of 5 days of scalp Video-EEG recordings (Nihon-

Kohden, Tokyo, Japan) no interictal epileptiform abnormalitieswere identified. Ictal patterns showed paroxysmal fast activitiesinvolving the right parasagittal electrodes (maximum amplitudeC4 > P4, and Cz > Pz). MRI suggested the presence of a focal malfor-mation within the right hemisphere. This potentially epileptogeniclesion was located anterior to the precentral sulcus extending deepinto the white matter. Clinical MEG revealed a single dense clusterof dipoles with uniform orientation located within the right infe-rior frontal sulcus and anterior to the precentral sulcus. In the mul-tidisciplinary patient management conference (PMC), a decisionwas made to proceed to intracranial EEG (ICEEG) monitoring tomap the extent of the epileptogenic cortex and its relationship toeloquent (motor) areas. Subdural grid electrodes were placed overthe fronto-parietal convexity focusing on the perirolandic cortexand the lateral, basal and mesial compartments of the frontal lobe.Furthermore, depth electrodes were implanted in the vicinity ofthe lesion. ICEEG evaluation demonstrated a localized focus withinthe right inferior frontal sulcus involving the inferolateral andbasal frontal regions and extending to the face motor area (seeFig. 2A).

3.1.2. TreatmentLimited (sublobar) resection of these regions sparing the hand

motor area (post-operative MRI shown in Fig. 2D) was undertaken4 years ago based on results of noninvasive and ICEEG data. Thepatient has remained seizure-free since.

3.1.3. FLA analysisFig. 2 shows the results of ICEEG, SECD clinical MEG analysis,

retrospective FLA analysis, and area of resection for this patient.Results of SECD and FLA analyses were co-registered to the pre-operative MRI as shown in Fig. 2B and C respectively. Both SECDand FLA localized the epileptogenic region to the inferior frontallobe anterior to the frontal operculum. A second adjacent regionof high information flow was observed by the FLA residing morerostrally within the resected inferior frontal lobe. Both SECD and

sed on resting state interictal MEG recordings is feasible irrespective of the016/j.clinph.2014.07.014

Fig. 2. Concordant results of clinical (ICEEG and MEG) and FLA-based evaluation on Patient 1 (focus in right hemisphere). The patient underwent a limited right frontallobectomy and has been seizure free for 4 years. (A) Map of intracranial EEG electrodes reconstructed on a 3D rendering of the patient’s MRI. Green filled circles denoteplacement of individual grid ICEEG electrodes; red filled area denotes ictal onset region as defined during ICEEG recordings. (B) Results of conventional single equivalentcurrent dipole analysis of clinical MEG data shown in a composite image. Locations and orientation of dipoles are denoted in yellow on the patient’s MRI (C) results of FLAanalysis of spontaneous MEG data superimposed on his MRI. The red area denotes the region of maximum inflow. (D) Axial slices of post-operative MRI. The area of resectionencompassed the overlapping regions as identified by ICEEG, clinical MEG, and FLA analysis. Note, all MRIs in this and subsequent figures are oriented in the ‘‘radiologicconvention,’’ i.e. the patient’s right side is on the left side of all axial and coronal images. (For interpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

4 B. Krishnan et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

FLA were concordant with the area of resection. Both regions iden-tified by FLA were resected.

3.2. Case 2: MRI negative, SECD and FLA concordant; right hemisphere

A 34 year-old left-handed male presented with intractable focalepilepsy starting at the age of 12 years. Seizures consisted of anaura described as a tingling sensation deep in the throat, whichwould spread to the left side of the face, followed by left face pull-ing and left hand posturing. During this period he would drool andhave difficulty speaking without alteration of awareness. Seizureswere brief in duration and occurred multiple times per day.

3.2.1. Presurgical evaluationNo interictal epileptiform abnormalities were identified during

prolonged scalp Video-EEG recordings. Several typical seizureswere captured in the epilepsy monitoring unit (EMU). Ictal EEGwas non-localizable due to the presence of copious EMG artifactsobscuring any underlying EEG changes. MRI was normal. InterictalPET showed subtle hypometabolism involving the right frontal andtemporal operculum. Ictal SPECT did not provide localizinginformation. Clinical MEG showed a tight cluster of dipoles withuniform orientation located within the right frontoparietal opercu-lum (see Fig. 3B). PMC recommended ICEEG evaluation withstereo-EEG (SEEG) electrodes targeting the right insula, and oper-cular regions to identify and delineate the seizure onset zone. SEEGshowed simultaneous seizure onset from the inferior precentraland parietal opercular regions (see Fig 3A).

3.2.2. TreatmentThe patient underwent limited right frontoparietal opercular

resection (Fig. 3D) and has been seizure free for 4 years.

3.2.3. FLA analysisRetrospective FLA analysis localized the region of abnormal

inflow near the bottom of the central sulcus and hence had sublo-bar concordance with the ICEEG-identified seizure onset zonewithin the fronto-parietal operculum (see Fig. 3C). Both SECDand FLA analyses were concordant with the area of resection.

Please cite this article in press as: Krishnan B et al. Epileptic focus localization bapresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1

3.3. Case 3: MRI positive, SECD and FLA concordant; left hemisphere

A 48 year-old left-handed man presented with medically intrac-table seizures starting at the age of 5 years. Seizures were mostlynocturnal and consisted of complex motor and hyperkinetic behav-iors without alteration of awareness occurring in clusters up to 4per night. Post-ictally he was unable to talk but aware of hissurroundings.

3.3.1. Presurgical evaluationProlonged interictal video-EEG recordings showed spikes

involving the left fronto-central region (maximum over theF3 > C3 electrodes). Most of the seizures recorded in the EMU werenon-localizable due to EMG artifacts. Few seizures showedincreased slowing and sharp wave activities in the left fronto-central region. MRI showed an area of T2/FLAIR hyperintensityresiding within the left inferior frontal sulcus associated with sub-tle blurring of the gray-white matter junction raising suspicion foran underlying focal cortical dysplasia. Ictal SPECT revealed areas ofhyperperfusion in the left inferior frontal and anterior insularregions. Interictal PET showed widespread cortical hypometabo-lism within the left hemisphere more pronounced in the leftinferolateral frontal and adjacent insular regions. Clinical MEGrevealed very frequent interictal spikes and polyspikes which wereMEG-unique, i.e. they had no identifiable EEG correlate during con-current scalp EEG recordings. ICEEG evaluation was performedwith a combination of depth electrodes targeting the inferiorfrontal sulcus and anterior insula, and subdural electrodes coveringthe inferior fronto-parietal region, as well as the basal frontal andanterior temporal neocortex. Interictal spikes and seizures wererecorded from the banks of the inferior frontal sulcus correspond-ing to the clinical MEG and MRI findings (see Fig. 4A and B).

3.3.2. TreatmentFunctional mapping using cortical stimulation revealed

eloquent cortex (Broca’s area) in close proximity to the ictal onsetzone. Hence, surgical resection of epileptogenic tissue was notrecommended for this patient.

3.3.3. FLA analysisFig 4B shows results of SECD analysis, which localized the

source of interictal discharges to the inferior and middle frontal

sed on resting state interictal MEG recordings is feasible irrespective of the016/j.clinph.2014.07.014

Fig. 3. Concordant results of clinical (ICEEG and MEG) and FLA based evaluation on Patient 2 (focus in right hemisphere). The patient underwent limited right opercularresection and has been seizure free for 4 years. (A) Map of intracranial EEG electrodes reconstructed on a 3D rendering of the patient’s MRI. Green filled dots denote location ofeach implanted stereo-EEG electrode. Red filled areas denote electrodes of ictal onset during ICEEG recordings. Axial and coronal slices of subfigure A depict the ICEEG-definedictal onset electrodes (in red). (B) Results of conventional single equivalent current dipole analysis of clinical MEG data shown in a composite image. Locations and orientationof dipoles are denoted in yellow on the patient’s MRI. (C) Results of FLA analysis of spontaneous MEG data superimposed on her MRI. The red area denotes the region ofmaximum inflow. (D) Axial slices of the patient’s post-operative MRI. The area of resection largely corresponds to the regions identified by ICEEG, clinical MEG, and FLAanalysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4. Results of clinical (ICEEG and MEG) and FLA based evaluation on Patient 3 (focus in left hemisphere). Surgical resection of the epileptogenic focus was notrecommended for this patient. (A) Map of intracranial EEG electrodes reconstructed on a 3D rendering of the patient’s MRI. Dots filled with green color denote location ofinsertion of stereo-EEG electrode. Red filled areas denote ictal onset electrodes. Axial and coronal slices with seizure onset zone marked in red is shown in the second panel ofsubfigure A. (B) Results of conventional single equivalent current dipole analysis of clinical MEG data shown in a composite image. Locations and orientation of dipoles aredenoted in yellow on the patient’s MRI. (C) Results of FLA analysis of spontaneous MEG data superimposed on her MRI. The red area denotes the region of maximum inflow.The area of ictal onset as revealed by ICEEG corresponded to region identified by SECD and FLA. (For interpretation of the references to colour in this figure legend, the readeris referred to the web version of this article.)

B. Krishnan et al. / Clinical Neurophysiology xxx (2014) xxx–xxx 5

gyri surrounding the inferior frontal sulcus. Fig 4C shows theresults of FLA analysis coregistered to the patient’s preoperativeMRI. Abnormal inflow is observed in the inferior frontal gyrusand is consistent with the concordant findings of ICEEG, MEGand MRI lesion.

3.4. Case 4: MRI negative, SECD discordant, FLA concordant; righthemisphere

A 42 year old right handed female presented with intractableright frontal lobe epilepsy. Seizures started at the age of 12 years.Seizures were characterized by twitching of the left fingers withloss of consciousness lasting for 1–2 min followed by post-ictalconfusion and garbled speech. These spells occurred on a dailybasis and evolved to secondarily generalized convulsive seizuresat least once a week.

3.4.1. Presurgical evaluationScalp Video-EEG showed right frontal spikes/polyspikes (maxi-

mum over F4-FZ) as well as bifrontal and generalized discharges.Ictal EEG patterns were preceded by a sharp wave with maximumnegativity at F4, followed by bifrontal delta slowing. Brain MRI wasnormal. Ictal-SPECT revealed areas of hyper-perfusion within theleft and right posterior insulae. Interictal FDG-PET showed diffuse

Please cite this article in press as: Krishnan B et al. Epileptic focus localization bapresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1

global cortical hypometabolism and bitemporal hypometabolismwithout definite asymmetry. Clinical MEG did not provide localiz-ing results due to the widespread distribution of recorded interictaldischarges. Nonetheless some of the scalp EEG findings raisedsuspicion of frontal lobe epilepsy possibly arising from the righthemisphere with rapid secondary bilateral synchrony. Bilateralimplantation with stereo-EEG electrodes was recommended to fur-ther explore this hypothesis. Depth electrodes were implanted tar-geting the right and left superior and middle frontal gyri includingthe supplementary motor area, right and left cingulate gyri andfronto-parietal opercular regions along with both frontal poles.ICEEG demonstrated that seizures were arising from the right ant-eromesial frontal lobe involving the right superior and middlefrontal gyri with rapid (within less than 0.5 s) propagation to thecontralateral frontal lobe (see Fig. 5A).

3.4.2. TreatmentA right premotor frontal lobectomy was performed (Fig. 5D).

The patient has been seizure free for 2 years.

3.4.3. FLA analysisRetrospective FLA analysis revealed region of maximum inflow

near the middle frontal gyrus which was concordant with the areaof resection (see Fig. 5C). SECD failed to localize the bulk of the

sed on resting state interictal MEG recordings is feasible irrespective of the016/j.clinph.2014.07.014

Fig. 5. Results of clinical (ICEEG and MEG) and FLA based evaluation on Patient 4 (focus in right hemisphere). The patient underwent limited right frontal lobectomy and hasbeen seizure free for 2 years. (A) Map of intracranial EEG electrodes reconstructed on top of the cortex. Green filled dots denote location of insertion of stereo-EEG electrodes.Red filled areas denote ictal onset electrodes. Axial and coronal slices with seizure onset zone marked in red is shown in the second panel of subfigure A. (B) Results ofconventional single equivalent current dipole analysis of the clinical MEG data shown in a composite image. Locations and orientation of dipoles are denoted in yellow on thepatient’s MRI. (C) Results of FLA analysis of spontaneous MEG data superimposed on her MRI. The red area denotes the region of maximum inflow. (D) Axial slices of thepatient’s post-operative MRI. ICEEG and FLA results are concordant. Clinical MEG was inconclusive. (For interpretation of the references to colour in this figure legend, thereader is referred to the web version of this article.)

Fig. 6. Results of MRI and FLA based evaluation on Patient 5 (focus in right hemisphere). No surgery was recommended for this patient. (A) Coronal and axial views of thepatient’s MRI. The red arrow points to the lesion in the right central sulcus, which is considered causative of this patient’s epilepsy. (B) Results of FLA analysis of the patient’sspontaneous MEG data superimposed on her MRI. The red area denotes the region of maximum inflow. Scalp video-EEG and clinical MEG studies were unremarkable. FLA andMRI were concordant with respect to location of the focus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of thisarticle.)

6 B. Krishnan et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

patient’s widespread interictal activities. Few of the morerestricted discharges were falsely localized within the left cingu-late gyrus or within the bi-occipital cortex based on SECD findings.None of these regions were included in the area of resection (seeFig. 5B).

3.5. Case 5: MRI positive, SECD indeterminate, FLA concordant; righthemisphere

A 64 year old right handed male presented with right peri-rolandic epilepsy starting at the age of 62 years. Discrete focalmotor seizures associated with left hand stiffening and jerkinglasting for 1–2 min with occasional secondary generalization werereported early on. When the patient was referred for video-EEGevaluation he had developed a clinical picture of chronic epilepsiapartialis continua (EPC), characterized by inability to control theleft hand during prolonged periods of recalcitrant myoclonusinvolving the wrist and fingers.

3.5.1. Presurgical evaluationScalp video-EEG failed to reveal any interictal or ictal

abnormalities during periods of EPC most likely due to therestricted nature of the epileptogenic generator in this patient.Nonetheless the patient exhibited persistent irregular twitches inindividual fingers of his left hand, which were present during restand on action, and could not be stopped by positioning. Brain MRIshowed a small area of localized T2 signal alteration within theright central sulcus (see Fig. 6A). Clinical MEG was unremarkableas no epileptic abnormalities were observed during the recordingsession.

Please cite this article in press as: Krishnan B et al. Epileptic focus localization bapresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1

3.5.2. TreatmentNo surgery was recommended for the patient given the locali-

zation of the putative MRI lesion within the primary (hand) motorarea. He has been maintained on high doses of two antiepilepticmedications.

3.5.3. FLA analysisDirectional connectivity analysis revealed a region of high

inflow within the right pre- and post-central gyri (see Fig. 6B).Increased inflow was also observed within the right middle frontalgyrus. Even though the scalp video-EEG and clinical MEG studieswere unremarkable, the FLA approach produced localizing resultswhich were quite consistent with the patient’s typical electroclin-ical presentation of EPC (arising from a perirolandic epileptogenicgenerator) and demonstrated sublobar concordance with the loca-tion of epileptogenic lesion on MRI.

4. Discussion

Our study illustrates the potential usefulness of directional con-nectivity in delineating the epileptogenic focus during resting-state interictal MEG recordings, particularly in cases where otherstudies (scalp-EEG and/or standard analysis of MEG) have beeninconclusive. The five patients reported in this series were selectedto examine the utility of FLA in a diverse spectrum of clinical sce-narios: (A) MRI-positive and concordant with clinical MEG findings(cases 1 and 3 – note the absence of scalp EEG spikes during con-current scalp EEG / MEG recordings, (B) MRI-negative and concor-dant with clinical MEG findings (case 2) (C) MRI-negative andclinical MEG that is discordant with the electroclinical hypothesis

sed on resting state interictal MEG recordings is feasible irrespective of the016/j.clinph.2014.07.014

B. Krishnan et al. / Clinical Neurophysiology xxx (2014) xxx–xxx 7

(case 4) and (D) MRI positive but MEG negative (no spikes) (case 5).The proposed FLA algorithm was successful in localizing the epi-leptic region in all five cases with all three operated patientsbecoming seizure-free after resective surgery. Specifically, thesecases illustrate the following points:

(A) FLA performed equally well in localizing the epileptogenicfocus in the three MEG-positive cases (1–3), as comparedto standard SECD modeling of the spikes captured duringMEG recording.

(B) FLA provided a localizing result in a patient whose clinicalMEG was inconclusive or misleading (case 4). This patienthad only infrequent (less than 5) sharp transients of ques-tionable clinical significance that were captured during the1-h MEG session. Coregistration of the corresponding SECDsources on the patient’s MRI showed that these activitieswere scattered over the bilateral occipital and parietalregions without conclusive localization. As a consequencethe clinical MEG results were not taken into account whensurgery was performed. FLA on the other hand accuratelylocalized the epileptic focus to the right middle frontalgyrus, which was indeed included in the area of resectionthat rendered the patient seizure-free. Case 4 also illustratesthe difficulties that skilled neurophysiologists encounter indifferentiating the ‘‘primary’’ irritative zone (‘‘red’’ spikesindicating the most ictogenic areas) (Dichter, 2009) fromreferred epileptiform transients (‘‘green’’ spikes) or fromsharply contoured ‘‘benign’’ transients of unclear clinicalsignificance.

(C) Case 5 demonstrates the potential of FLA to localize the sei-zure focus in the absence of any visually identifiable epilep-tiform abnormalities in the MEG recording.

In case 5, we presented a patient whose semiology is consistentwith EPC defined as ‘‘spontaneous and regular or irregular clonicmuscular twitching affecting a limited part of the body occurringfor a minimum of one hour and recurring at intervals of no morethan ten seconds’’ (Thomas et al., 1977; Obeso et al., 1985), whichis considered as a specific form of focal motor status implicatingthe perirolandic cortex (Alexopoulos and Jones, 2011). In fact thesite of origin of EPC is attributed to cortical elements residingwithin the primary motor area as evident from studies showingepileptiform EEG potentials recorded by surface or subdural elec-trodes time-locked to the muscle jerks (Thomas et al., 1977;Wieser et al., 1978; Obeso et al., 1985; Cockerell et al., 1996). Asin case 5, surface EEG may not be able to discern these activitiesgiven the restricted nature of the cortical generator within the per-irolandic region. In a similar manner the conventional MEG wasnot helpful due to the absence of epileptiform abnormalities. TheFLA algorithm provides an alternative strategy in delineating theepileptic focus is such challenging cases of suspected ongoinginterictal/ictal discharges, which cannot be resolved with surfaceEEG (and would otherwise require well-targeted invasive record-ings for electrophysiological confirmation). From Fig 6, we canobserve that the highest inflow as detected by FLA is located withinthe right pre/post central gyrus, which is in agreement with theobserved MRI lesion and the patient’s typical clinical presentation.

This work is a direct extension of our previous study of connec-tivity analysis applied on intracranial EEG recordings, which pro-duced promising results (Vlachos et al., 2013) in a series of 9patients with intractable temporal lobe epilepsies. Previous studieshave shown that the dynamics of critical ‘‘normal’’ brain sites arerecruited by the epileptogenic focus long (minutes to tens of min-utes) before seizure onset, and tend to get progressively more syn-chronized in conjunction to the hyperexcitable focus until a seizureoccurs to break this abnormally long and persistent bond of

Please cite this article in press as: Krishnan B et al. Epileptic focus localization bapresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1

dynamics (Iasemidis et al., 1990, 2003; Iasemidis and Sackellares,1991; Sackellares et al., 2000; Tsakalis and Iasemidis, 2006;Chakravarthy et al., 2007; Sabesan et al., 2009).

In light of these results, we applied measures of dynamics forfocus localization during the interictal period. The rationale for thisapproach is based on the fact that the focus is presumably kept atbay interictally by controlling neuronal networks. As a result theepileptogenic generator should be discernible as the region thatexhibits maximum information inflow from other brain regionsduring the majority of the interictal period. This hypothesis wastested using intracranial EEG data in patients with intractable tem-poral lobe epilepsy in whom the epileptic focus exhibited the high-est inflow as compared to all other regions of the brain that werebeing sampled by intracranial electrodes (Vlachos et al., 2013).

The results provide support to our initial hypothesis that theepileptic focus has functionally abnormal information inflow fromsurrounding neural structures during the resting (interictal) stateas suggested by thalamo-cortical neuronal population models ofepilepsy (Tsakalis and Iasemidis, 2006; Chakravarthy et al., 2007)and validated by our previous work on intracranial EEG (Vlachoset al., 2013). Routine clinical studies in the EMU, currently, disre-gard the resting state electrophysiological signals and focus oninterictal epileptiform abnormalities and ictal EEG patterns duringhabitual seizures. Using advanced signal processing techniques wehereby provide proof-of-concept that resting state physiologicalsignals do carry important information pertinent to the localiza-tion of the epileptogenic generator in patients with drug resistantepilepsy, and should be targeted for the advancement of diagnosticand therapeutic strategies in these patients.

5. Limitations

Being a data driven methodology, performance of FLA will sufferwhen the quality of the recorded MEG data is poor. Different fac-tors – such as excessive head movements within the MEG array,or magnetic activities of medical devices used by the patient –might severely deteriorate the quality of the recorded MEG data.Preprocessing of MEG data should be carried out prior to the appli-cation of FLA on these data sets. The effects of MVAR model order,radius (r) used for estimating neighborhood, system noise, brainstates (such as sleep transitions, drowsiness), medication has tobe systematically investigated for further validation of the FLAalgorithm. Also, the present study was conducted in a limited num-ber of patients with uni-focal epilepsies. Moreover, our results can-not be generalized without further validation with retrospectiveand ideally prospective studies of patients undergoing MEG forpresurgical localization and subsequent epilepsy surgery. In thecurrent design, FLA is bound to perform poorly in case of bifocalabnormalities in homologous regions as statistical significance ofinformation flows is derived referentially to homologous regionsbetween hemispheres. Improvement of FLA for application in bifo-cal epilepsy is currently underway. We are in the process of per-forming a systematic retrospective study to test the usefulness ofFLA on the most challenging group of MRI-negative (‘‘nonlesional’’)patients with intractable focal epilepsy, who underwent successfulepilepsy surgery at our center.

6. Conclusion

We present a proof-of-concept study on the use of a novel focuslocalization algorithm applied to noninvasive interictal-only MEGdata. This focus localization algorithm has conceptual advantagesover traditional point-based models like SECD, as it does not relyon the presence of epileptiform abnormalities during MEG acquisi-tion. This critical advantage of FLA could enhance the yield of

sed on resting state interictal MEG recordings is feasible irrespective of the016/j.clinph.2014.07.014

8 B. Krishnan et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

clinical MEG studies by providing a solution to MEG-negative datasets. If successful, this approach could lead to a paradigm shift inthe diagnosis and treatment of epilepsy by analyzing interictal-only, noninvasive MEG data obtained in the outpatient setting –without the need for hospital admission and costly long-termvideo-EEG studies which presuppose the recording of ictal events.Additionally, FLA is a computationally inexpensive post-processingalgorithm that can be easily incorporated into routine clinical prac-tice with minimum cost. Application of FLA to a larger number ofpatients with different epilepsy manifestations and etiologies iscurrently underway in our laboratory.

Conflict of interest

None.

References

Achard S, Salvador R, Whitcher B, Suckling J, Bullmore ED. A resilient, low-frequency, small-world human brain functional network with highly connectedassociation cortical hubs. J Neurosci 2006;26:63–72.

Alexopoulos AV, Jones SE. Focal motor seizures, epilepsia partialis continua, andsupplementary sensorimotor seizures. In: Wyllie E, Cascino GD, editors. Wyllie’streatment of epilepsy: principle and practice. 5th ed. Philadelphia,PA: Lippincott Williams & Wilkins; 2011. p. 163–83.

Alkawadri R, Krishnan B, Kakisaka Y, Nair D, Mosher JC, Burgess RC, et al.Localization of the ictal onset zone with MEG using minimum norm estimate ofa narrow band at seizure onset versus standard single current dipole modeling.Clin Neurophysiol 2013;124:1915–8.

Astolfi L, Cincotti F, Mattia D, Marciani MG, Baccala LA, Fallani FDV, et al. Assessingcortical functional connectivity by partial directed coherence: simulations andapplication to real data. Biomed Eng IEEE Trans 2006;53:1802–12.

Astolfi L, Cincotti F, Mattia D, Marciani MG, Baccala LA, de Vico Fallani F, et al.Comparison of different cortical connectivity estimators for high-resolution EEGrecordings. Hum Brain Mapp 2007;28:143–57.

Baccalá LA, Sameshima K. Partial directed coherence: a new concept in neuralstructure determination. Biol Cybern 2001;84:463–74.

Baccala LA, Sameshima K, Takahashi DY. Generalized partial directed coherence. In:15th int. conf. digit. signal process; 2007. p. 163–6.

Baillet S, Mosher JC, Leahy RM. Electromagnetic brain mapping. IEEE Signal ProcessMag 2001;18:14–30.

Bassett DS, Bullmore E. Small-world brain networks. Neuroscientist2006;12:512–23.

Chakravarthy N, Sabesan S, Iasemidis L, Tsakalis K. Controlling synchronization in aneuron-level population model. Int J Neural Syst 2007;17:123–38.

Cockerell OC, Rothwell J, Thompson PD, Marsden CD, Shorvon SD. Clinical andphysiological features of epilepsia partialis continua cases ascertained in theUK. Brain 1996;119:393–407.

Cohen D, Cuffin BN. Demonstration of useful differences betweenmagnetoencephalogram and electroencephalogram. Electroencephalogr ClinNeurophysiol 1983;56:38–51.

Dichter MA. Emerging concepts in the pathogenesis of epilepsy and epileptogenesis.Arch Neurol 2009;66:443–7.

Huang MX, Mosher JC, Leahy RM. A sensor-weighted overlapping-sphere headmodel and exhaustive head model comparison for MEG. Phys Med Biol1999;44:423.

Iasemidis L, Sackellares J. The evolution with time of the spatial distribution of thelargest lyapunov exponent on the human epileptic cortex. In: Duke D, PritchardW, editors. Measuring chaos in human brain. Singapore: World Scientific Pub CoInc.; 1991. p. 49–82.

Iasemidis LD. Seizure prediction and its applications. Neurosurg Clin N Am2011;22:489–506.

Iasemidis LD, Chris Sackellares J, Zaveri HP, Williams WJ. Phase space topographyand the lyapunov exponent of electrocorticograms in partial seizures. BrainTopogr 1990;2:187–201.

Iasemidis LD, Principe JC, Sackellares JC. Measurement and quantification of spatio-temporal dynamics of human epileptic seizures. In: Akay M, editor. Nonlinearbiomedical signal processing. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2000.p. 294–318.

Iasemidis LD, Shiau DS, Chaovalitwongse W, Sackellares JC, Pardalos PM, Principe JC,et al. Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng2003;50:616–27.

Please cite this article in press as: Krishnan B et al. Epileptic focus localization bapresence or absence of spikes. Clin Neurophysiol (2014), http://dx.doi.org/10.1

Insel TR, Landis SC, Collins FS. Research priorities. The NIH brain initiative. Science2013;340:687–8.

Kakisaka Y, Wang ZI, Mosher JC, Dubarry A-S, Alexopoulos AV, Enatsu R, et al.Clinical evidence for the utility of movement compensation algorithm inmagnetoencephalography: successful localization during focal seizure. EpilepsyRes 2012a;101:191–6.

Kakisaka Y, Wang ZI, Mosher JC, Nair DR, Alexopoulos AV, Burgess RC.Magnetoencephalography’s higher sensitivity to epileptic spikes mayelucidate the profile of electroencephalographically negative epilepticseizures. Epilepsy Behav 2012b;23:171–3.

Knake S, Halgren E, Shiraishi H, Hara K, Hamer HM, Grant PE, et al. The value ofmultichannel MEG and EEG in the presurgical evaluation of 70 epilepsypatients. Epilepsy Res 2006;69:80–6.

Knowlton RC, Laxer KD, Aminoff MJ, Roberts TPL, Wong STC, Rowley HA.Magnetoencephalography in partial epilepsy: clinical yield and localizationaccuracy. Ann Neurol 1997;42:622–31.

Krishnan B, Vlachos I, Wang Z, Mosher J, Iasemidis L, Burgess R, et al. Advanced MEGsource analysis for epileptogenic focus localization in patients with non-lesional MRI. In: 29th south. biomed. eng. conf.; 2013. p. 57–8.

Larson-Prior LJ, Oostenveld R, Della Penna S, Michalareas G, Prior F, Babajani-FeremiA, et al. Adding dynamics to the human connectome project with MEG.Neuroimage 2013;80:190–201.

Michalareas G, Schoffelen J-M, Paterson G, Gross J. Investigating causality betweeninteracting brain areas with multivariate autoregressive models of MEG sensordata. Hum Brain Mapp 2013;34:890–913.

Nenonen J, Nurminen J, Kicic D, Bikmullina R, Lioumis P, Jousmäki V, et al.Validation of head movement correction and spatiotemporal signal spaceseparation in magnetoencephalography. Clin Neurophysiol 2012;123:2180–91.

Obeso JA, Rothwell C, Marsden CD. The spectrum of cortical myoclonus: from focalreflex jerks to spontaneous motor epilepsy. Brain 1985;108:193–224.

Pataraia E, Simos PG, Castillo EM, Billingsley RL, Sarkari S, Wheless JW, et al. Doesmagnetoencephalography add to scalp video-EEG as a diagnostic tool inepilepsy surgery? Neurology 2004;62:943–8.

Sabesan S, Chakravarthy N, Tsakalis K, Pardalos P, Iasemidis L. Measuring resettingof brain dynamics at epileptic seizures: application of global optimization andspatial synchronization techniques. J Comb Optim 2009;17:74–97.

Sackellares J, Iasemidis L, Shiau D, Gilmore R, Roper S. Epilepsy – when chaos fails.In: Lehnertz K, Arnhold J, Grassberger P, Elger C, editors. Chaos in brain? 2000. p.112–33.

Schneider F, Alexopoulos AV, Wang ZI, Almubarak S, Kakisaka Y, Jin K, et al.Magnetic source imaging in non-lesional neocortical epilepsy: additional valueand comparison with ICEEG. Epilepsy Behav 2012;24:234–40.

Silfverhuth MJ, Remes J, Starck T, Nikkinen J, Veijola J, Tervonen O, et al. Directionalconnectivity of resting state human fMRI data using cascaded ICA-PDC analysis.Acta Radiol 2011;52:1037–42.

Stefan H, Hummel C, Hopfengärtner R, Pauli E, Tilz C, Ganslandt O, et al.Magnetoencephalography in extratemporal epilepsy. J Clin Neurophysiol2000;17:190–200.

Stefan H, Hummel C, Scheler G, Genow A, Druschky K, Tilz C, et al. Magnetic brainsource imaging of focal epileptic activity: a synopsis of 455 cases. Brain.2003;126:2396–405.

Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: a user-friendlyapplication for MEG/EEG analysis. Comput Intell Neurosci 2011;2011:1–13.

Taulu S, Hari R. Removal of magnetoencephalographic artifacts with temporalsignal-space separation: demonstration with single-trial auditory-evokedresponses. Hum Brain Mapp 2009;30:1524–34.

Taulu S, Kajola M, Simola J. Suppression of interference and artifacts by the signalspace separation method. Brain Topogr 2004;16:269–75.

Thomas JE, Reagan TJ, Klass DW. Epilepsia partialis continua: a review of 32 cases.Arch Neurol 1977;34:266.

Tsakalis K, Iasemidis LD. Control aspects of a theoretical model for epilepticseizures. Int J Bifurc Chaos 2006;16:2027–32.

Vlachos I, Krishnan B, Sirven J, Noe K, Drazkowski J, Iasemidis L. Frequency-basedconnectivity analysis of interictal iEEG to localize the epileptogenic focus. 29thSouthern Biomedical Engineering Conference, Miami; 2013. p. 163–4.

Wang ZI, Alexopoulos AV, Nair D, Krishnan B, Mosher JC, Burgess RC, et al.Feasibility of magnetoencephalography recording in an epilepsy patient withimplanted responsive cortical stimulation device. Clin Neurophysiol2013;124:1705–6.

Watts DJ, Strogatz SH. Collective dynamics of small-world networks. Nature1998;393:440–2.

Wieser HG, Graf HP, Bernoulli C, Siegfried J. Quantitative analysis of intracerebralrecordings in epilepsia partialis continua. Electroencephalogr Clin Neurophysiol1978;44:14–22.

Zhdanov A, Wilenius J, Paetau P, Ahonen A, Makela JP. Quantifying the contributionof video in combined video-magnetoencephalographic ictal recordings ofepilepsy patients. Epilepsy Res 2013;105:405–9.

sed on resting state interictal MEG recordings is feasible irrespective of the016/j.clinph.2014.07.014