Current and emerging potential for magnetoencephalography in pediatric epilepsy

Review Article

Current and emerging potential for

magnetoencephalography in pediatric epilepsy

Christos Papadelisa,b,*, Chellamani Harinib, Banu Ahtama,c, Chiran Doshia,b, Ellen Granta,c,d,e

and Yoshio Okadaa,b

aFetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Harvard Medical

School, Boston, MA, USAbDepartment of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USAcDivision of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School,

Boston, MA, USAdDepartment of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USAeDepartment of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical

Imaging, Charlestown, MA, USA

Received 15 March 2013

Revised 18 May 2013

Accepted 18 May 2013

Abstract. Magnetoencephalography (MEG) is a noninvasive neuroimaging tool that is increasingly becoming useful for presurgical

delineation of epileptogenic zones and eloquent cortex in both lesional and non-lesional pediatric cases. During the past 10 yrs, the

use of MEG in pediatric epilepsy research has increased. This paper starts with a review of the use of MEG in pediatric epilepsy. We

then describe the protocol used for epilepsy patients at the pediatric MEG facility in Boston Children’s Hospital and present two case

studies of intractable epilepsy obtained in our laboratory -cortical dysplasia and tuberous sclerosis complex -to illustrate our metho-

dology in localizing epileptiform generators. In both cases, we are able to localize generators of interictal spikes in the irritative zone

just outside the lesion. We also present results on localization of the somatosensory cortex based on our pediatric MEG system to

illustrate the utility of MEG for identification of the eloquent cortex. We complete this review by considering advantages and limita-

tions of MEG in children with epilepsy, its future developments and research applications. Application of MEG in pediatric epilepsy

will accelerate during the coming years as different types of pediatric whole-head MEG systems and more advanced data analysis

methods become available to the researchers and clinicians. These advances will lead to greater use of MEG as a complement to clin-

ical electroencephalography, with improved noninvasive delineation of the epileptogenic zone.

Keywords: Magnetoencephalography, pediatric epilepsy, tuberous sclerosis complex, cortical dysplasia, pediatric magnetoence-

phalography systems, human brain development

1. Introduction

Childhood epilepsy is a common neurological dis-

order with a prevalence rate of 4–6 per 1,000 children

[1]. It can have a major impact on the development of

children [2] and may significantly affect their later adult

life. Long term follow up studies in childhood-onset epi-

lepsy indicate favorable outcome in about two-thirds of

children [3,4]. The International League Against Epi-

lepsy task force has defined drug resistant epilepsy as

failure of adequate trials of two tolerated, appropriately

chosen and used antiepileptic drug schedules (whether

*Corresponding author: Christos Papadelis, Boston Children’s

Hospital, Harvard Medical School, Department of Neurology, 9 Hope

Avenue, Waltham, MA 02453 USA. Tel.: +1 781 216 1128; Fax: +1

781 216 1172; E-mail: [email protected].

Journal of Pediatric Epilepsy 2 (2013) 73–85DOI 10.3233/PEP-13040IOS Press

73

2146-457X/13/$27.50 © 2013 – IOS Press and the authors. All rights reserved

as monotherapies or in combination) to achieve sus-

tained seizure freedom [5]. This helps to select pat-

ients who can be evaluated further for possible

epilepsy surgery. It has been estimated that approxi-

mately 30% of epilepsy patients become medically

intractable [6–9] and some may eventually require

resective epileptic surgery. To be successful, epileptic

surgery should achieve a seizure-free state with mini-

mal or no functional deficits. This requires (i) careful

delineation of the epileptogenic zone, and (ii) ana-

tomic localization of the eloquent cortex [10]. The

epileptogenic zone is the ‘area of cortex that is indis-

pensable for the generation of epileptic seizures’ [11].

The epileptogenic zone in practice refers to the mini-

mum amount of tissue that needs to be resected to

ensure seizure freedom [12].

Crucial to the success of surgical treatment is the

availability of a robust pre-surgical marker that identi-

fies the epileptogenic zone and the functionally rele-

vant eloquent cortex. Techniques to estimate the

epileptogenic zone can be traced back to ancient times

[13]. Nowadays, multimodal imaging and neuro-

physiological tests are used in pediatric epilepsy to

presurgically define the epileptogenic zone and the

eloquent cortex. Magnetic resonance imaging (MRI)

[14], MRI with post processing techniques such as

voxel based morphometry, and diffusion tensor imaging

(DTI) [15] have revolutionized the presurgical work-up

by enabling improved detection of the epileptogenic

lesion. Non-invasive functional neuroimaging methods,

such as positron emission tomography [16], single-

photon emission computed tomography [17], and func-

tional MRI [18], have permitted improved localization

of the functional deficit zone and the relevant eloquent

cortex.

Magnetoencephalography (MEG) [19] is consid-

ered as one of the promising tools to localize and

visualize sources of epileptic activity in the pediatric

population. It has been used clinically in adults since

1980s to characterize the irritative zone and, in some

studies, the ictal onset zone. The irritative zone is

the region that produces interictal epileptogenic dis-

charges [10]. The ictal-onset zone is the region where

seizures are generated. MEG is particularly useful in

patients with normal MRI findings [20,21] that repre-

sent up to 40% of epilepsy cases undergoing presur-

gical evaluation [22] and usually have less favorable

surgical outcomes compared to patients with focal

epileptogenic lesions seen on MRI [23]. Patients with

multifocal or diffuse disease may also benefit [24,25]

from MEG.

2. Basic principles of MEG

MEG is a non-invasive electrophysiological ima-

ging technique used to measure extremely weak mag-

netic fields produced by electrical currents in active

neurons of the human brain. The magnetic field of the

earth is ~50 microTesla (0.5 × 10-4 T). MEG signals

are on the order of 10 picoTesla to 10 femtoTesla

(1 × 10-11T − 1 × 10-14 T). Thousands of nearby neu-

rons that are in a similar orientation have to act simul-

taneously for their magnetic field to be measurable at

the scalp [26,27]. Synchronized neuronal activity is

best observed at the cortical pyramidal cells that

are aligned perpendicular to the surface of the cortex

[28–30]. The main sources of the magnetic fields

recorded by MEG are considered to be the postsynap-

tic currents in the apical dendrites of these cortical pyr-

amidal cells [31–35], though recent studies have

challenged this notion by showing that action potentials

may also be recorded [36–42]. MEG is most sensitive

to the currents that are tangential to the surface of the

scalp. Magnetic fields produced by radial sources

do not usually come out to the surface of the head

[29,43–45]. MEG offers an excellent temporal resolu-

tion in the range of sub-milliseconds and a very good

localization accuracy of a few millimeters, especially

for superficial cortical sources [46].

The MEG equipment is located inside magnetically

shielded rooms (MSR) where the recordings are per-

formed. Magnetically shielded rooms are equipped

with adjustable lighting and audio-visual communi-

cation systems that allow communication with the

technicians seated outside. Shielded environments

minimize the interference of MEG recordings from

external electromagnetic sources (i.e. power lines,

radiofrequency signals from portable devices, electri-

cal devices and computers, magnetic fields from mov-

ing magnetized objects such as cars, elevators, and

trains). Brain activity is measured by positioning the

patient head inside a helmet that contains magnetic

field detection coils which are inductively coupled

to very sensitive magnetic field detection devices

called superconducting quantum interference devices

(SQUIDs). The assembly consisting of a detection

coil and a SQUID is made of superconductors, which

lose electrical resistivity below a critical temperature,

near the temperature (–268.95 °C) of liquid helium.

SQUIDs convert the magnetic flux passing through

the detection coils into voltage changes [47,48]. Modern

MEG systems are usually equipped with a high number

of SQUIDs (more than 300) offering simultaneous

74 C. Papadelis et al. / Potential for MEG in pediatric epilepsy

recording of brain activity from different parts of

the brain.

The goal of this paper is to give a review of the use

of MEG in pediatric epilepsy, to describe the protocol

used for epilepsy patients at the pediatric MEG facility

in Boston Children’s Hospital, to consider advantages

and limitations of MEG in children with epilepsy,

and to discuss future developments and research appli-

cations. Some earlier review papers are available on the

use of MEG in pediatric epilepsy [49–51].

3. Protocol for pediatric epilepsy MEG

3.1. Equipment

Our laboratory is equipped with a pediatric MEG

system, called BabySQUID, which was designed by

one of us (Yoshio Okada) and built by Tristan Inc.

(San Diego, CA, USA) [52]. The system is installed

in an MSR (Fig. 1). BabySQUID is designed to take

advantage of the thin scalp and skull of newborns and

infants, which may be as thin as 2–3 mm on the dorsal

surface at birth [53]. The detection coils are placed

just 7–10 mm below the headrest, where the head of a

patient is placed for testing (Fig. 1a). This gap is

approximately half the gap for conventional adult

MEG systems, making signal strengths at the sensors

stronger by a factor of about four since the signal

strength increases as inverse of square of the gap.

The short gap also provides higher spatial resolution,

since it is approximately linear with the inverse of the

gap. BabySQUID can measure MEG signals with 76

axial gradiometers (Fig. 1b) within an oval region of

interest (ROI) of 12–14 cm in diameter (Fig. 1a).

High-density electroencephalography (ECG), electroo-

culography (EOG), and electrocardiography recordings

are routinely recorded simultaneously with the MEG

signals. The EEG signals are recorded with a 128-

channel EEG system (ANT-Neuro, Netherlands) by

using caps suitable for toddlers and children. MEG and

volumetric MRI coregistration are performed using an

in-house procedure, described in Section 3.2. For the cor-

egistration, we use a 6-degree-of-freedom commercial

motion tracking device (FASTRAK, Polhemus Inc.,

USA) located in our preparation room, and an opti-

cal tracking system (Polaris, Northern Digital Inc.,

Canada) located in the MSR (Fig. 1c).

3.2. Patient preparation

Patient preparation is minimal, and the examination

is generally well tolerated. The entire session lasts

about 2–3 hrs with the actual measurements lasting

around 60 min. We ask parents to bring the child sleep

deprived by either skipping a nap or waking her/him up

Fig. 1. The BabyMEG facility at Boston Children’s Hospital at Waltham. Upper left panel (a): The headrest where the child places her/his head

during the recordings. Lower left panel (b): Schematic diagram of the partial coverage sensor array consists of 76 axial gradiometers. Right panel

(c): Overview of the BabySQUID system inside the shielded room. A stereoscopic Polaris camera is used for coregistration of the anatomical

data with the functional data.

C. Papadelis et al. / Potential for MEG in pediatric epilepsy 75

earlier in the morning, and let the child go to sleep in our

facility once the preparation procedure is completed.We

do not use sedation for our recordings. Patients change

their clothing to a non-metallic hospital gown. The

child’s head is then digitized with the FASTRAK sys-

tem. If the patient is old enough, s/he will be asked

to sit on a chair where the FASTRAK transmitter is

attached. The FASTRAK receiver is placed securely

on patient’s head with a headband (Fig. 2a). If the

patient is an infant or a toddler, s/he lies down on a

changing bed. With a non-toxic washable marker, basic

anatomical landmarks on the face and the ears are

marked such as the nasion, left and right pre-auricular

points, the forehead, the cheeks, and the chin (Fig. 2a).

The assistant then uses the stylus to digitally mark the

basic anatomical landmarks that were previously identi-

fied on the face and ears. Beyond the basic anatomical

landmarks, additional points are also recorded to com-

pute an accurate transformation matrix between the

FASTRAK and the Polaris systems (Fig. 2b).

The EEG cap, EOG and ECG electrodes are placed

on patient’s head, face and chest, respectively. EEG

electrode locations are digitized with the digital pen,

and the well of each electrode is filled with conduc-

tive gel. Once asleep, we bring the patient on top of

the bed of the BabySQUID cart. Parents can monitor

their child via a liquid crystal display screen located

outside the MSR. The assistant is always in the MSR

during the recording. S/he places the patient’s head on

the headrest and coordinates the recording sessions.

The previously marked anatomical landmarks on the

face are digitized with the stylus of the Polaris system

before each recording. If the patient moves her/his head

after the digitization, the assistant repeats the procedure.

Since the BabySQUID has a partial coverage, it allows

recordings only from a specific ROI each time. Based

on our experience with this system, the coverage is large

enough for us to localize cortical generators of MEG

signals below the array. The placement of the particular

ROI is guided by previous EEG findings from long-term

EEG monitoring recordings.

3.3. Data recordings and analysis

3.3.1. Data recordings and preprocessing

MEG, EEG, and peripheral recordings are performed

at a sampling rate of 1,024 Hz for at least four runs, each

lasting 10 mins, while the patient is asleep. The data are

then preprocessed and visually inspected for interictal

MEG and EEG sharp waves as well as bursts of rhyth-

mic activity by an experienced epileptologist. Abnormal

interictal events are classified based on similar topo-

graphic characteristics, and then averaged in order to

increase the signal to noise ratio (SNR) of the MEG

signal. Source localization is estimated for both aver-

aged (per category) as well as single events. For MEG

and EEG data analysis, two software packages are

used: Brainstorm [54] and MNE-Suite (MinimumNorm

Estimation-Suite) [55]. Coregistration is performed by

in-house software developedby one of us (ChiranDoshi).

Biological artifacts, such as heartbeats, are removed by

the signal-space projection algorithm implemented in

BrainStorm software [56].

3.3.2. Source localization

Source localization requires solving the forward and

inverse problems of bioelectromagnetics. The forward

Fig. 2. Coregistration procedure. (a) Marks made with a non-toxic washable marker on anatomical landmarks of the face of a child. These points are

digitized by both Polhemus and Polaris systems. (b) Magnetic resonance images – extracted head surface of a child (on gray) coregistered with

digitized points extracted by Polaris system and the BabySQUID sensor array (yellowish surface covering the left somatosensory cortex).


problem in MEG has a unique solution. It is calculated

using the Maxwell equations, the magnetic field mea-

sured outside the scalp from a known distribution of

neural activity generators. It is important to have an

accurate forward model because MEG source modeling

analyses make a comparison of the measured data with

the predicted signals by the model [47] and the solution

of the forward problem is often a prerequisite for source

localization analyses [47].

The estimation of the most probable intra-cranial

current sources in the brain from the observed extra-

cranial MEG recordings is called the ‘inverse problem’

[47,48]. The inverse problem is an ill-posed problem,

meaning that it does not have a unique solution. This

is due to the fact that a specific set of measured bioe-

lectric signals at the sensor level can be generated by

an infinite number of source generator configurations

[55]. A priori knowledge about the source generators

is thus necessary to constrain the inverse problem solu-

tion. There are two main categories of source localiza-

tion methods: (i) the equivalent current dipole (ECD)

[57], and (ii) the distributed source modeling. The

ECD model assumes that the MEG signal is generated

by one (or more) focal sources. Each source is then

described by an infinitesimally small line current ele-

ment and the task is to find the location, direction and

moment of each ECD. The distributed source modeling

methods assume that the locations of the source dipoles,

and sometimes their orientations, are fixed, while their

amplitudes and/or orientations are estimated from the

measured data. The ECD is extensively used in epilepsy

studies, while distributed source models have started

to be applied more recently in the analysis of MEG

epilepsy data.

In our laboratory, we use three source imaging algo-

rithms in order to localize the interictal activity and the

burst rhythmic activity: the single-dipole ECD, the whi-

tened minimum norm estimates (wMNE) [55], and the

dynamic statistical parametric mapping (dSPM) [58].

The MNE algorithm is one of the earliest algorithms

developed for cortical source imaging and is based on

the L2-norm minimization. Its main limitation is its

low resolution and the bias it introduces towards super-

ficial sources. The wMNE is a modification of MNE,

which rectifies the bias issue to some extent. However,

low resolution of wMNE remains to be a problem.

dSPM has recently been introduced [58] in the study

of epilepsy. It provides spatiotemporal source distri-

bution with millisecond temporal resolution by impos-

ing anatomical information about the cortical surface

derived from MRI and noise normalization of the

minimum norm estimate. Clinical studies using

dSPM in patients with partial epilepsy suggest that

it has advantages over a single-dipole model in ana-

lyzing interictal MEG spikes [59,60]. Because the

technical details of each imaging algorithm are

beyond the scope of this article, we provide here

only a brief summary of their basic concepts.

For estimating the forward model, we currently use

the symmetric boundary element method (BEM) that

incorporates information from three realistic layers

(scalp, inner skull, outer skull). The OpenMEEG soft-

ware is used for the estimation of the BEM forward

model [61,62]. BEM is a commonly used forward

model for both MEG and EEG, because of its speed

and modest demands for computer memory. To analyze

the MEG and high-density EEG data from our epilepsy

pediatric patients, we calculate the ECD, MNE, and

dSPM at the peak of both averaged as well as single

interictal spikes for the purpose of estimating the spatio-

temporal cortical source distribution. Source localiza-

tion solutions are mapped onto both a cortical surface

and a volumetric image. The ECDs with goodness of

fit > 70% are considered adequate as possible cortical

sources. Virtual or synthetic depth electrodes, con-

structed by appropriate mathematic weighting of the

MEG sensors, are ‘placed’ at locations of epileptogenic

activity. They are used to identify the time-activity pro-

file at these locations allowing the morphological char-

acterization of the regional electric activity.

3.3.3. MRI and DTI Imaging

Whole-brain MRI is performed on our standard

clinical 3T system. We collect MRI data from those

patients who do not have an MRI scan close (<6 mo)

to the time of the MEG/EEG recording. This is in order

to estimate an accurate forward model. For accurately

localizing sources of neuronal activity from MEG/

EEG recordings, it is necessary to co-register MEG/

EEG data with structural MRI data. Our imaging proto-

col includes a multi-echo T1-weighted magnetization-

prepared rapid-acquisition gradient-echo sequence, using

1mm3 isotropic voxel size; in addition, echo-planar based

navigators are added in each repetition time (TR) to

correct for potential patient motion. Diffusion imaging

is acquired in the axial plane, using 30 images with

b = 1000 s/mm2 and ten images with b = 0 s/mm2, isotro-

pic voxel size of 2 mm3. If time permits and if the subject

is cooperative, additional diffusion directions and multi-

ple b-values can be obtained for a better quality image.

The overall acquisition time of the MRI and DTI data


do not exceed 30 mins. DTI data from our patients are

not presented in this paper.

3.3.4. Case studies

3.3.4.1. Temporal lobe dysplasia patient

A 3-year-old girl with uncomplicated birth and peri-

natal history presented with her first seizure when she

was 2.5-years-old. She had complex partial seizure char-

acterized by behavioral arrest, unresponsive with left

sided head and eye deviation and both hands fisting.

MRI revealed abnormal T2 signal involving the subcor-

tical and deep white matter of the left temporal lobe and

overlying cortex without post-contrast enhancement,

consistent with a cortical malformation/migrational

abnormality. Interictal EEG had multifocal spikes over

the left hemisphere in the left temporal parietal region,

occasional right temporal spikes, and rare spikes in the

left frontal region. Ictal EEG captured eight clinical sei-

zures consisting of leftward head deviation. The seizure

onset was broadly in the left hemisphere, not well loca-

lized. Fourteen electrographic seizures were also identi-

fied, all having onset in the left temporo-parietal region

(electrodes P3 or P3/P7), except one with broad left

hemisphere seizure with maximum at left anterior tem-

poral region. Ictal single-photon emission computed

tomography indicated hyper-perfusion surrounding the

persistently hypo-perfused region of cortical dysplasia

in the left temporal lobe.

Twenty minutes of MEG data were recorded during

sleep. The sensor array covered the left temporal and

left anterior parietal lobes. Two groups of interictal

spikes were classified based on the topography of the

measured activity at the sensor array. The first group

of interictal spikes was localized in the proximity of

the lesion in the left temporal lobe. The second group

was localized at the left parietal cortex. Fig. 3 presents

two interictal events from the first group of activity

(Fig. 3a), the topography of activity at the peak of the

first event indicating a dipolar pattern (Fig. 3b), and

their localization by using the wMNE (Figs. 3c, 3d,

and 3e). The localization of these events was consis-

tently in the proximity of the lesion as it was indicated

by both wMNE and ECD methods (Fig. 4).

3.3.4.2. Tuberous sclerosis complex (TSC) patient

A 4-years-old girl with refractory epilepsy, as a result

of TSC, with uncomplicated perinatal history presen-

ted with her first seizure when she was 4-month-old.

Cardiac rhabdomyoma, revealed by echocardiography,

resulted in the diagnosis of TSC1 mutation. MRI

showed multifocal cortical tubers in the bilateral fron-

tal, parietal and occipital lobes. Mineralization of the

cortical tuber in the right parieto-occipital lobe was

Fig. 3. Magnetoencephalography (MEG) data from a child with temporal lobe dysplasia. (a) MEG sensor time traces for 3 secs of recordings.

Two interictal spikes are identified (green arrow indicates one). (b) MEG activity topography at the peak of the first spike indicated with a green

arrow in (a). (c and d) whitened minimum norm estimates (wMNE) source localization of the single spike localized at the proximity of the tem-

poral lobe lesion (marked with yellow dashed line). (e) wMNE source localization results projected at the cortical surface of the patient.


also observed. Routine and ambulatory EEG indicated

very frequent right posterior temporal/occipital sharp

waves (electrodes P8/O2). Left frontal spikes were also

observed along with intermittent left frontal slowing

(electrode F3). One-hour MEG and high-density EEG

data were recorded during sleep. The sensor array was

covering the parieto-occipital lobe for 40 min and the

left fronto-temporal lobe for 20 min. A high number of

interictal spikes (>50) were detected with a consistent

spatiotemporal pattern indicating a focal right parieto-

occipital source. The spikes were averaged and a high

SNR was achieved. MEG data from single as well as

averaged data indicated a focal source in the proximity

of a large tuber in the upper right quadrant (Fig. 5).

ECDs of single events were also localized at the same

location (>20 spikes in a 1-cm area) (not shown).

3.3.5. Localization of eloquent cortex

3.3.5.1. Somatosensory projection areas

We have consistently found that MEG is useful for

identifying the eloquent cortex. Here we illustrate the

results for somatosensory projection areas in the pri-

mary somatosensory (SI) cortex. The tip of the fingers

(Digits 1, 3 and 5) of the right hand and of the right

big toe were stimulated with a tactile stimulator by

applying a brief air puff (10 ms) to a plastic membrane

which expanded physically to stimulate the skin. The

activations in the contralateral somatosensory cortex

were recorded with BabySQUID. Fig. 6 shows the

results for one subject. The projection sites are all

located along the posterior bank of the central sulcus

with the well-known somatotopic arrangement. These

results can be easily obtained from each subject by aver-

aging 50–100 responses, requiring 100–200 sec per site

of stimulation. The capability of MEG for source locali-

zation has been well demonstrated during the past 20 yrs

and presurgical localization of eloquent cortices is a

clinically approved procedure.

Fig. 4. Magnetoencephalography source localization data of interic-

tal activity with the use of equivalent current dipoles and whitenedminimum norm estimates for multiple single events and averaged

data respectively. Both techniques localized the interictal activity

sources at the proximity of the temporal lobe lesion.

Fig. 5. Magnetoencephalography data from a child with tuberous sclerosis complex. Upper panel: averaged interictal spikes (left) with the same

spatiotemporal profile, and its corresponding whitened minimum norm estimates source localization (center and right). The interictal activity was

localized at the proximity of the tubor. Lower panel: one of the interictal spikes (left) used to estimate the averaged signal at the upper panel, and

its corresponding whitened minimum norm estimates source localization (center and right). There is a consistency between the localization of

averaged and single event.


4. Current clinical applications in pediatric

epilepsy

MEG is predominantly used for the localization of

interictal activity in the investigation of children with

both lesional [63] and non-lesional cases [20,64–66].

The MEG source localization of interictal activity

(clusters and scatters) predicts the irritative zone

observed on the intracranial video-EEG recordings

[51]. MEG spike sources that form a single cluster

(≥20 spikes in 1-cm area) indicate that the distribution

of sources correlates with the seizure-onset zone (SoZ),

part of the symptomatogenic zone and irritative zone

observed on intracranial video EEG recordings [67,68].

Thus, the addition of MEG to the clinical evaluation of

medically refractory epilepsy patients can help to gener-

ate a hypothesis regarding epileptogenic foci in epilepsy

patients and may improve the postsurgical outcomes of

the patients [69–72].

Although MEG is predominantly used for the loca-

lization of interictal activity, an increasing number of

recent studies report the localization of ictal activity

by using multichannel MEG systems, which was con-

firmed by intraoperative Electrocorticography (ECoG)

[69,73–76]. Ictal MEG in pediatric population demon-

strates good concordance with the SoZ as defined by

the current gold standards: intracranial EEG and surgi-

cal outcome [77]. To capture a seizure during a clinical

MEG recording is however methodologically chall-

enging because (i) the time of MEG recordings in clini-

cal setup is limited (1–2 h) decreasing significantly the

possibility of capturing a seizure; (ii) the movement arti-

facts that occur frequently with seizures can be detri-

mental to the quality of MEG recording [78]; (iii) both

ictal MEG and EEG signals often have a low SNR,

which may not allow for accurate source localization

analysis, and (iv) the magnetic field attenuates rapidly

as the distance from focus to MEG sensors increases,

and distant sources in the mesial temporal cortex

[79,80] as well as those in the basal temporal, in the

basal frontal, and in the deep interhemispheric areas

might go undetected. Recent studies have proposed

new signal processing tools considering also the fre-

quency content of the ictal MEG activity for detecting

and localizing these events [81]. The investigation of

the magnetic field manifestations at the seizure onset

can provide valuable information about the SoZ, though

several studies have reported that ictal MEG occasion-

ally failed to localize the SoZ [69,74,75].

5. Advantages and limitations of MEG in

children with epilepsy

Compared to other neuroimaging methods, MEG

presents a unique set of significant advantages but

also limitations compared to EEG. Preparation for an

MEG recording is faster and easier. With MEG, the

brain activity can be measured immediately after pla-

cing the children’s head inside the special helmet,

since there is no need to attach any sensors over the

head. Unlike EEG signals [84], MEG signals are not

distorted by skull conductivity [82,83]. MEG is also

less distorted than EEG by unfused regions of the cra-

nial bone such as fontanel or suture [30]. Moreover,

MEG signals are reference free providing absolute

measurements of the magnetic field produced by the

brain, in contrast to EEG, which provides potential

differences between two locations. MEG signals can

be measured with a high density of magnetic field

sensors [52]. In contrast, high-density EEG always

faces the problem of salt bridge between the electro-

des when the head is small like in children, thus limit-

ing significantly the number of EEG sensors that can

be used in children.

Although MEG does have the aforementioned advan-

tages, the issue of relative localization capabilities of

MEG and EEG is still a matter of debate. The locali-

zation accuracy of scalp EEG and MEG have been

compared in studies with computer simulations,

phantom constructions resembling the human brain,

artificial dipoles implanted in epilepsy patients during

Fig. 6. Source localization results (whitened minimum norm esti-

mates) for tactile responses from stimulation of digits 1, 3, 5, and

big toe. Black line: central sulcus.


presurgical evaluation, and invasive EEG recordings

[85]. Phantom studies have reported that MEG offers

higher (almost x2) localization accuracy than EEG

[86,87]. When artificial dipoles were implanted in epi-

lepsy patients, MEG localization was slightly better

than the localization of EEG (8 and 10 mm for MEG

and EEG respectively). However, computer simulation

studies have surprisingly reported that EEG is more

accurate than MEG for the same number of sensors

averaged over many source locations and orientations

[88]. MEG preferentially records activity from tangen-

tial sources, thus recording activity predominantly from

sulci. MEG provides an excellent localization accuracy

of a few mm for superficial tangential generators [46]

up to the level where it is possible to determine the

cytoarchitectonic identity of a brain region [46,89].

There is no evidence that scalp EEG can reach such a

high level of localization accuracy for superficial corti-

cal sources, even if an equal number of sensors are used.

In clinical setup, the localization of the epileptogenic

zone is most commonly defined noninvasively with

video-EEG using scalp electrodes. Video-EEG usually

records data from a relatively low number of electrodes

(~20) compared to most modern MEG systems that

record electromagnetic signals from a high number of

sensors (usually >300). Thus, the MEG localization

accuracy cannot be reached by the standard video-

EEG recordings in typical clinical settings, even if we

assume that the forward problem is successfully solved.

However, EEG is more sensitive than MEG for radial

sources [30].

There are some distinct limitations of MEG. Video-

EEG provides long recordings increasing the possibi-

lity of detecting and recording ictal events. Capturing

ictal events with MEG, though feasible [75,76,90,91],

can be difficult due to cooperation of the child, cost,

and access to the facility. In addition, unlike EEG,

MEG sensors can not conform to the shape of head

of each individual since the helmet and sensor array

within the helmet are all fixed in shape. This is a ser-

ious limitation of MEG for pediatric research since

head size increases with age, especially during the

first 2 yrs after full-term birth.

Compared to the ECoG, which is the gold standard

in the localization of epileptogenic region, MEG

potentially has an advantage in pediatric patients in

whom long-term invasive monitoring is not possible

or challenging. ECoG recording has some risks for

producing infection and bleeding. MEG can simulta-

neously and non-invasively detect and record cortical

activity from the entire cerebral cortex with a large

number of sensors, while the investigation of the

ROI is limited in ECoG to the area of craniotomy.

At a practical level,MEG ismore expensive than EEG

to set up and operate in a clinical environment. The con-

struction of anMEG facility is about 20 to 50 times more

expensive than the cost of setting up an EEG laboratory.

The operation cost is also quite high for MEG since

all the commercial systems use liquid helium.

6. Future of MEG in pediatric epilepsy research

We predict that the use of MEG in basic and clinical

studies of pediatric epilepsy will accelerate during the

next several years as new MEG instruments are intro-

duced. First of all, over the next 2–3 yrs whole-head

pediatric MEG systems will become available. Most

of the pediatric epilepsy work based on MEG has been

carried out using adult MEG systems that are subopti-

mal for the pediatric population. Recently, a whole-head

MEG system was completed based on the BabySQUID

we have been using and installed at Children’s Hospital

of Philadelphia [92]. This is similar to the BabySQUID

in design, but it provides a whole-head instead of a

regional coverage. One of us (Yoshio Okada) has been

developing a second-generation pediatric whole-head

MEG system based on the BabySQUID in collaboration

with Tristan Inc. This system – babyMEG – is scheduled

for installation at our hospital during the fall of 2013. As

results start to appear in the literature based on these sys-

tems, we expect other sites will install a similar system.

There are currently more than 10 sites in the world

active in MEG-based pediatric epilepsy research – all

of them except our group have been using adult MEG

systems.

The operating cost of MEG facilities will also

decrease. The biomagnetic industry has recognized the

need to reduce the operating cost of MEG systems.

Therefore, some companies are now developing portable

helium recyclers that can reliquefy evaporating helium

gas (e.g. GWR Instruments, San Diego, CA). Others

have been developing 100% helium recycling systems

(e.g. [93]; Elekta-Neuromag, Oy, Helsinki, Finland).

In addition to the improvements on the pediatric

whole-head MEG systems based on low-temperature

(L-Tc) SQUIDs, we anticipate seeing other types of

MEG instruments that can provide comparable quality

of data from children. These new instruments eliminate

some of the basic limitations of the conventional MEG.

One possibility is the development of a pediatric MEG

system based on high-temperature (H-Tc) SQUIDs.


Recently, it has been shown that it should be possible

to build an MEG system with a system noise compar-

able to that of L-Tc SQUID MEG systems, but using

H-Tc SQUIDs (operating at liquid nitrogen tempera-

ture of –196.15 °C) [94]. This type of system reduces

the operating costs because it uses liquid nitrogen.

Such a system can be attached to a liquid nitrogen recy-

cler, which recycles 100% of liquid nitrogen. Another

possibility is the introduction of a whole-head MEG

system based on atomic magnetometers that are small

enough to be used like an array of EEG electrodes [95].

The array based on miniature atomic magnetometers

can be used like EEG with the shape of the array made

to conform to the head shape of individual children.

The atomic magnetometers operate at room tempera-

ture without requiring liquid nitrogen or helium. The

sensitivity of these miniature atomic magnetometers

operating at room temperature is approaching the level

of MEG systems based on L-Tc SQUIDs.

We also see advances in the software clinicians will

have access to for analyzing electrophysiological data

in pediatric epilepsy. Today when MEG data are col-

lected, results are displayed in the form similar to spon-

taneous EEG data, namely as a continuous stream of

data showing spontaneous brain activity. The results

may be averaged over similar responses to increase data

quality for event-related cortical activity and are then

shown at the sensor level that is in terms of waveforms

measured by each sensor. The data become more useful

when they are seen as activities projected onto the

brain or in some regions within the brain. This techni-

que is generally called magnetic source imaging (MSI).

Although this concept was introduced many years ago,

MSI has remained as an off-line process requiring a long

period of data analysis. We predict that the field will see

strong advances in the area of real-time MSI that will

allow the clinicians to see source images in the brain

immediately after MEG measurements – this feature

should help expedite clinical decision making (e.g.

[96]). We see that all of these advances will lead to

greater use of MEG in pediatric epilepsy in the near

future.

References

[1] Cowan LD, Bodensteiner JB, Leviton A, Doherty L. Prevalence

of the epilepsies in children and adolescents. Epilepsia 1989;

30(1):94–106.

[2] Stores G. School-children with epilepsy at risk for learning and

behaviour problems. DevMed Child Neurol 1978;20(4): 502–8.

[3] Sillanpää M, Schmidt D. Natural history of treated childhood-

onset epilepsy: prospective, long-term population-based study.Brain 2006;129(Pt 3):617–24.

[4] Geerts A, Arts WF, Stroink H, Peeters E, Brouwer O, Peters B,

et al. Course and outcome of childhood epilepsy: a 15-year

follow-up of the Dutch Study of Epilepsy in Childhood.

Epilepsia 2010;51(7):1189–97.

[5] Kwan P, Arzimanoglou A, Berg AT, Brodie MJ, Allen

Hauser W, Mathern G, et al. Definition of drug resistant

epilepsy: consensus proposal by the ad hoc Task Force of the

ILAE Commission on Therapeutic Strategies. Epilepsia 2010;

51(6):1069–77.

[6] Engel J Jr. Etiology as a risk factor for medically refractory

epilepsy: a case for early surgical intervention. Neurology

1998;51(5):1243–4.

[7] Kwan P, Brodie MJ. Early identification of refractory epi-

lepsy. N Engl J Med 2000;342(5):314–9.

[8] Berg AT, Shinnar S, Levy SR, Testa FM, Smith-Rapaport S,

Beckerman B. Early development of intractable epilepsy in

children: a prospective study. Neurology 2001;56(11):1445–52.[9] Farrell K, Wirrell E, Whiting S. The definition and prediction

of intractable epilepsy in children. Adv Neurol 2006;97:

435–42.

[10] Datta A, Loddenkemper T. The epileptogenic zone. In:Wyllie E,

Cascino G, Gidal B, Goodkin H, editors. Wyllie’s treatment

of epilepsy. Principles & practice. 5th ed. Philadelphia:

Lippincott, Williams & Wilkins, 2011; p 818–27.

[11] Rosenow F, Lüders H. Presurgical evaluation of epilepsy.

Brain 2001;124(Pt 9):1683–700.

[12] Jayakar P. Invasive EEG monitoring in children: when,

where, and what? J Clin Neurophysiol 1999;16(5):408–18.

[13] Temkin O. The falling sickness: A history of epilepsy from

the Greeks to the beginnings of modern neurology. 2nd ed.

Baltimore: Johns Hopkins University Press, 1994.

[14] Durá-Travé T, Yoldi-Petri ME, Esparza-Estaún J, Gallinas-

Victoriano F, Aguilera-Albesa S, Sagastibelza-Zabaleta A.

Magnetic resonance imaging abnormalities in children with

epilepsy. Eur J Neurol 2012;19(8):1053–9.[15] Widjaja E, Geibprasert S, Otsubo H, Snead OC 3rd, Mah-

moodabadi SZ. Diffusion tensor imaging assessment of the

epileptogenic zone in children with localization-related epi-

lepsy. AJNR Am J Neuroradiol 2011;32(10):1789–94.

[16] Sood S, Chugani HT. Functional neuroimaging in the pre-

operative evaluation of children with drug-resistant epilepsy.

Child’s Nerv Syst 2006;22(8):810–20.

[17] Kaminska A, Chiron C, Ville D, Dellatolas G, Hollo A,

Cieuta C, et al. Ictal SPECT in children with epilepsy: com-

parison with intracranial EEG and relation to postsurgical

outcome. Brain 2003;126(Pt 1):248–60.

[18] Wilke M, Pieper T, Lindner K, Dushe T, Staudt M, Grodd W,

et al. Clinical functional MRI of the language domain

in children with epilepsy. Hum Brain Mapp 2011;32(11):

1882–93.

[19] CohenD.Magnetoencephalography: evidence ofmagnetic fields

produced by alpha-rhythm currents. Science 1968;161(3843):

784–6.[20] RamachandranNair R, Otsubo H, Shroff MM, Ochi A,

Weiss SK, Rutka JT, et al. MEG predicts outcome following

surgery for intractable epilepsy in children with normal or

nonfocal MRI findings. Epilepsia 2007;48(1):149–57.

[21] Wennberg R. Magnetic source imaging versus intracranial

electroencephalogram: Neocortical versus temporolimbic epi-

lepsy surgery. Ann Neurol 2006;60(2):271.


[22] Carne RP, O’Brien TJ, Kilpatrick CJ, MacGregor LR, Hicks

RJ, Murphy MA, et al. MRI-negative PET-positive temporallobe epilepsy: a distinct surgically remediable syndrome.

Brain 2004;127(Pt 10):2276–85.

[23] Blume WT, Ganapathy GR, Munoz D, Lee DH. Indices of

resective surgery effectiveness for intractable nonlesional

focal epilepsy. Epilepsia 2004;45(1):46–53.

[24] Shibasaki H, Ikeda A, Nagamine T. Use of magnetoencepha-

lography in the presurgical evaluation of epilepsy patients.

Clin Neurophysiol 2007;118(7):1438–48.

[25] Kamimura T, Tohyama J, Oishi M, Akasaka N, Kanazawa O,

Sasagawa M, et al. Magnetoencephalography in patients with

tuberous sclerosis and localization-related epilepsy. Epilepsia

2006;47(6):991–7.

[26] Okada YC, Nicholson C, Llinas R. Magnetoencephalography

(MEG) as a new tool for non-invasive realtime analysis of nor-

mal and abnormal brain activity in humans. In: Ottoson D,

RosteneW, editors. Visualization of brain functions. NewYork:

Stockton Press, 1989; p. 245–66.

[27] Okada YC, Wu J, Kyuhou S. Genesis of MEG signals in amammalian CNS structure. Electroencephalogr Clin Neuro-

physiol 1997;103(4):474–85.

[28] Okada YC. Neurogenesis of evoked magnetic fields, somato-

sensory evoked magnetic field, motor field, auditory evoked

field, visual evoked field and endogenous magnetic field. In:

Williamson SJ, Romani GL, Kaufman L, Modena I, editors.

An interdisciplinary approach. Biomagnetism. New York:

Plenum Press, 1983; p. 399–468.

[29] Okada YC, Nicholson C. Magnetic evoked field associated

with transcortical currents in turtle cerebellum. Biophys J

1988;53(5):723–31.

[30] Lew S, Sliva D, Choe M-S, Grant PE, Okada Y, Wolters CH,

et al. Effect of sutures and fontanels on MEG and EEG

source analysis with an infant FEM head model. Neuroimage

(in press).

[31] Wu J, Okada YC. Physiological bases of the synchronized

population spikes and slow wave of the magnetic field gener-

ated by a guinea-pig longitudinal CA3 slice preparation.Electroencephalogr Clin Neurophysiol 1998;107(5):361–73.

[32] Wu J, Okada YC. Roles of a potassium afterhyperpolariza-

tion current in generating neuromagnetic fields and field

potentials in longitudinal CA3 slices of the guinea-pig. Clin

Neurophysiol 1999;110(11):1858–67.

[33] Wu J, Okada YC. Roles of calcium- and voltage-sensitive

potassium currents in the generation of neuromagnetic signals

and field potentials in a CA3 longitudinal slice of the guinea-

pig. Clin Neurophysiol 2000;111(1):150–60.

[34] Murakami S, Zhang T, Hirose A, Okada YC. Physiological

origins of evoked magnetic fields and extracellular field

potentials produced by guinea-pig CA3 hippocampal slices.

J Physiol 2002;544(Pt 1):237–51.

[35] Murakami S, Hirose A, Okada YC. Contribution of ionic cur-

rents to magnetoencephalography (MEG) and electroence-

phalography (EEG) signals generated by guinea-pig CA3

slices. J Physiol 2003;553(Pt 3):975–85.

[36] Curio G, Mackert BM, Burghoff M, Koetitz R, Abraham-Fuchs K, Härer W. Localization of evoked neuromagnetic

600 Hz activity in the cerebral somatosensory system. Elec-

troencephalogr Clin Neurophysiol 1994;91(6):483–7.

[37] Hashimoto I, Mashiko T, Imada T. Somatic evoked high-

frequency magnetic oscillations reflect activity of inhibitory

interneurons in the human somatosensory cortex. Electroen-

cephalogr Clin Neurophysiol 1996;100(3):189–203.

[38] IkedaH, Leyba L, BartoloA,WangY,OkadaYC. Synchronized

spikes of thalamocortical axonal terminals and cortical neuronsare detectable outside the pig brain with MEG. J Neurophysiol

2002;87(1):626–30.

[39] Ikeda H, Wang Y, Okada YC. Origins of the somatic N20

and high-frequency oscillations evoked by trigeminal stimu-

lation in the piglets. Clin Neurophysiol 2005;116(4):827–41.

[40] Murakami S, Okada Y. Contributions of principal neocortical

neurons to magnetoencephalography and electroencephalo-

graphy signals. J Physiol 2006;575(Pt 3):925–36.

[41] Kimura T, Ozaki I, Hashimoto I. Impulse propagation along

thalamocortical fibers can be detected magnetically outside

the human brain. J Neurosci 2008;28(47):12535–8.

[42] Papadelis C, Leonardelli E, Staudt M, Braun C. Can magneto-

encephalography track the afferent information flow along

white matter thalamo-cortical fibers? Neuroimage 2012;60(2):

1092–105.

[43] Geselowitz, DB. On the magnetic field generated outside an

inhomogeneous volume conductor by internal current sources.

IEEE Trans Magn 1970;6:346–7.[44] Grynszpan F, Geselowitz DB. Model studies of the magneto-

cardiogram. Biophys J 1973;13(9):911–25.

[45] Cohen D, Hosaka H. Part II: magnetic field produced by a

current dipole. J Electrocardiol 1976;9(4):409–17.

[46] Papadelis C, Poghosyan V, Fenwick PB, Ioannides AA.

MEG’s ability to localise accurately weak transient neural

sources. Clin Neurophysiol 2009;120(11):1958–70.

[47] Hamalainen M. Hari R. Magnetoencephalographic characteriza-

tion of dynamic brain activation: Basic principles and methods

of data collection and source analysis. In: Toga AW, Mazziotta

JC, editors. Brain mapping: The methods. San Diego: Academic

Press; 2002; p. 227–53.

[48] Paetau R. Magnetoencephalography in pediatric neuroima-

ging. Dev Sci 2002;5(3):361–70.

[49] Grondin R, Chuang S, Otsubo H, Holowka S, Snead OC 3rd,

Raybaud C, et al. The role ofmagnetoencephalography in pedia-

tric epilepsy surgery. Child’s Nerv Syst 2006;22(8):779–85.

[50] Schwartz ES, Dlugos DJ, Storm PB, Dell J, Magee R, FlynnTP, et al. Magnetoencephalography for pediatric epilepsy:

how we do it. AJNR Am J Neuroradiol 2008;29(5):832–7.

[51] Ochi A, Otsubo H. Magnetoencephalography-guided epilepsy

surgery for children with intractable focal epilepsy: SickKids

experience. Int J Psychophysiol 2008;68(2):104–10.

[52] Okada Y, Pratt K, Atwood C, Mascarena A., Reineman R,

Nurminen J, et al. BabySQUID: a mobile, high-resolution

multichannel MEG system for neonatal brain assessment.

Rev Sci Instrum 2006;77:24301–9.

[53] Hansman CF. Growth of interorbital distance and skull thick-

ness as observed in roentgenographic measurements. Radiology

1966;86(1):87–96.

[54] Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM.

Brainstorm: a user-friendly application for MEG/EEG analy-

sis. Comput Intell Neurosci 2011;2011:879716.

[55] Hämäläinen MS, Ilmoniemi RJ. Interpreting magnetic fields

of the brain: minimum norm estimates. Med Biol Eng Com-

put 1994;32(1):35–42.[56] Nolte G, Curio G. The effect of artifact rejection by signal-

space projection on source localization accuracy in MEG

measurements. IEEE Trans Biomed Eng 1999;46(4):400–8.

[57] Hämäläinen M, Hari R, Ilmoniemi R, Knuutila J, Lounasmaa O.

Magnetoencephalography-theory, instrumentation, and appli-

cations to noninvasive studies of the working human brain.

Rev Mod Phys 1993;65:1–93.


[58] Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW,

Lewine JD, et al. Dynamic statistical parametric mapping:combining fMRI and MEG for high-resolution imaging of

cortical activity. Neuron 2000;26(1):55–67.

[59] ShiraishiH,Ahlfors SP, StufflebeamSM,TakanoK,OkajimaM,

Knake S, et al. Application of magnetoencephalography in

epilepsy patients with widespread spike or slow-wave

activity. Epilepsia 2005;46(8):1264–72.

[60] Shiraishi H, Stufflebeam SM, Knake S, Ahlfors SP, Sudo A,

Asahina N, et al. Dynamic statistical parametric mapping for

analyzing the magnetoencephalographic epileptiform activity

in patients with epilepsy. J Child Neurol 2005;20(4):363–9.

[61] Gramfort A, Papadopoulo T, Olivi E, Clerc M. OpenMEEG:

opensource software for quasistatic bioelectromagnetics.

Biomed Eng Online 2010;9:45.

[62] Kybic J, Clerc M, Abboud T, Faugeras O, Keriven R,

Papadopoulo T. A common formalism for the integral for-

mulations of the forward EEG problem. IEEE Trans Med

Imaging 2005;24(1):12–28.

[63] Kim H, Lim BC, Jeong W, Kim JS, Chae JH, Kim KJ, et al.Magnetoencephalography in pediatric lesional epilepsy surgery.

J Korean Med Sci 2012;27(6):668–73.

[64] Otsubo H, Ochi A, Elliott I, Chuang SH, Rutka JT, Jay V, et al.

Snead OC. MEG predicts epileptic zone in lesional extrahippo-

campal epilepsy: 12 pediatric surgery cases. Epilepsia 2001;

42(12):1523–30.

[65] Bast T, Oezkan O, Rona S, Stippich C, Seitz A, Rupp A,

et al. EEG and MEG source analysis of single and averaged

interictal spikes reveals intrinsic epileptogenicity in focal cor-

tical dysplasia. Epilepsia 2004;45(6):621–31.

[66] Seo JH, Holland K, Rose D, Rozhkov L, Fujiwara H, Byars A,

et al. Multimodality imaging in the surgical treatment of

children with nonlesional epilepsy. Neurology 2011;76(1):

41–8.

[67] Iida K, Otsubo H, Mohamed IS, Okuda C, Ochi A, Weiss SK,

et al. Characterizing magnetoencephalographic spike sources

in children with tuberous sclerosis complex. Epilepsia 2005;

46(9):1510–7.[68] Oishi M, Kameyama S, Masuda H, Tohyama J, Kanazawa O,

Sasagawa M, et al. Single and multiple clusters of magne-

toencephalographic dipoles in neocortical epilepsy: signifi-

cance in characterizing the epileptogenic zone. Epilepsia

2006;47(2):355–64.

[69] Shiraishi H, Watanabe Y, Watanabe M, Inoue Y, Fujiwara T,

Yagi K. Interictal and ictal magnetoencephalographic study

in patients with medial frontal lobe epilepsy. Epilepsia

2001;42(7):875–82.

[70] Tang L, Mantle M, Ferrari P, Schiffbauer H, Rowley HA,

Barbaro NM, et al. Consistency of interictal and ictal onset

localization using magnetoencephalography in patients with

partial epilepsy. J Neurosurg 2003;98(4):837–45.

[71] Stufflebeam SM, Tanaka N, Ahlfors SP. Clinical applications

of magnetoencephalography. Hum Brain Mapp 2009;30(6):

1813–23.

[72] Knowlton RC. Can magnetoencephalography aid epilepsy

surgery? Epilepsy Curr 2008;8(1):1–5.[73] Stefan H, Schneider S, Feistel H, Pawlik G, Schüler P,

Abraham-Fuchs K, et al. Ictal and interictal activity in

partial epilepsy recorded with multichannel magnetoelec-

troencephalography: correlation of electroencephalography/

electrocorticography, magnetic resonance imaging, single

photon emission computed tomography, and positron emis-

sion tomography findings. Epilepsia 1992;33(5):874–87.

[74] Tilz C, Hummel C, Kettenmann B, Stefan H. Ictal onset loca-

lization of epileptic seizures by magnetoencephalography.Acta Neurol Scand 2002;106(4):190–5.

[75] Eliashiv DS, Elsas SM, Squires K, Fried I, Engel J Jr. Ictal

magnetic source imaging as a localizing tool in partial epi-

lepsy. Neurology 2002;59(10):1600–10.

[76] Assaf BA, Karkar KM, Laxer KD, Garcia PA, Austin EJ,

Barbaro NM, et al. Ictal magnetoencephalography in temporal

and extratemporal lobe epilepsy. Epilepsia 2003;44(10):1320–7.

[77] Fujiwara H, Greiner HM, Hemasilpin N, Lee KH, Holland-

Bouley K, Arthur T, et al. Ictal MEG onset source localiza-

tion compared to intracranial EEG and outcome: improved

epilepsy presurgical evaluation in pediatrics. Epilepsy Res

2012;99(3):214–24.

[78] Medvedovsky M, Taulu S, Gaily E, Metsähonkala EL,

Mäkelä JP, Ekstein D, et al. Sensitivity and specificity of sei-

zure-onset zone estimation by ictal magnetoencephalography.

Epilepsia 2012;53(9):1649–57.

[79] Baumgartner C, Pataraia E, Lindinger G, Deecke L. Neuro-

magnetic recordings in temporal lobe epilepsy. J Clin Neuro-physiol 2000;17(2):177–89.

[80] Enatsu R, Mikuni N, Usui K, Matsubayashi J, Taki J, Begum T,

et al. Usefulness of MEG magnetometer for spike detection in

patients with mesial temporal epileptic focus. Neuroimage

2008 Jul 15;41(4):1206–19.

[81] Yagyu K, Takeuchi F, Shiraishi H, Nakane S, Sueda K,

Asahina N, et al. The applications of time-frequency analyses

to ictal magnetoencephalography in neocortical epilepsy.

Epilepsy Res 2010;90(3):199–206.

[82] Barth DS, Sutherling W, Broffman J, Beatty J. Magnetic

localization of a dipolar current source implanted in a sphere

and a human cranium. Electroencephalogr Clin Neurophysiol

1986;63(3):260–73.

[83] Okada YC, Lahteenmäki A, Xu C. Experimental analysis of

distortion of magnetoencephalography signals by the skull.

Clin Neurophysiol 1999;110(2):230–8.

[84] Flemming L, Wang Y, Caprihan A, Eiselt M, Haueisen J,

Okada Y. Evaluation of the distortion of EEG signals causedby a hole in the skull mimicking the fontanel in the skull of

human neonates. Clin Neurophysiol 2005;116(5):1141–52.

[85] Baumgartner C. Controversies in clinical neurophysiology.

MEG is superior to EEG in the localization of interictal

epileptiform activity: Con. Clin Neurophysiol 2004;115(5):

1010–20.

[86] GharibS,SutherlingWW,NakasatoN,BarthDS,BaumgartnerC,

AlexopoulosN, et al.MEGandECoG localization accuracy test.

Electroencephalogr Clin Neurophysiol 1995;94(2):109–14.

[87] Leahy RM, Mosher JC, Spencer ME, Huang MX, Lewine

JD. A study of dipole localization accuracy for MEG and

EEG using a human skull phantom. Electroencephalogr Clin

Neurophysiol 1998;107(2):159–73.

[88] Liu AK, Dale AM, Belliveau JW. Monte Carlo simulation

studies of EEG and MEG localization accuracy. Hum Brain

Mapp 2002;16(1):47–62.

[89] Papadelis C, Eickhoff SB, Zilles K, Ioannides AA. BA3b and

BA1 activate in a serial fashion after median nerve stimulation:direct evidence from combining source analysis of evoked fields

and cytoarchitectonic probabilistic maps. Neuroimage 2011;

54(1):60–73.

[90] Oishi M, Otsubo H, Kameyama S, Wachi M, Tanaka K,

Masuda H, et al. Ictal magnetoencephalographic discharges

from elementary visual hallucinations of status epilepticus.

J Neurol Neurosurg Psychiatry 2003;74(4):525–7.


[91] Yoshinaga H, Ohtsuka Y, Watanabe Y, Inutsuka M, Kita-

mura Y, Kinugasa K, et al. Ictal MEG in two children withpartial seizures. Brain Dev 2004;26(6):403–8.

[92] Edgar JC, Paulson P, Eugene H, Kevin P, Anthony M, Paul M,

et al. Artemis 123: Development of a whole-head infant

MEG system. 18th International Conference on Biomagnetism,

August 26th–30th, 2012, Paris, France.

[93] Takeda T, Okamoto M, Atsuda K, Kobayashi A, Owaki T,

Katagiri K. An efficient helium circulation system with small

GM cryocoolers. Cryogenics 2008;48(1–2):6–11.

[94] Poppe U, Dunin-Borkowski RE, Schiek M, Boers F,

Chocholacs H, Dammers J, et al. High-Tc DC SQUIDs formagnetoencephalography. IEEE Trans Appl Superconduct

2013;23(3):1600705.

[95] Sander TH, Preusser J, Mhaskar R, Kitching J, Trahms L,

Knappe S. Magnetoencephalography with a chip-scale atomic

magnetometer. Biomed Opt Express 2012;3(5):981–90.

[96] Sudre G, Parkkonen L, Bock E, Baillet S, Wang W, Weber DJ.

rtMEG: a real-time software interface for magnetoencepha-

lography. Comput Intell Neurosci 2011;2011:327953.


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