magnetoencephalography is feasible for infant assessment...

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Experimental Neurology

Magnetoencephalography is feasible for infant assessment of

auditory discrimination

Marie Cheoura,b,c,*, Toshiaki Imadad,e, Samu Tauluf, Antti Ahonenf,

Johanna Salonena,c, Patricia Kuhld

aDepartment of Psychology, University of Miami, Coral Gables, FL 33124-0751, USAbDepartment of Pediatrics, University of Miami School of Medicine, Miami, FL 33124, USA

cBioMag Laboratory, Helsinki University Central Hospital, Helsinki, FinlanddInstitute for Learning and Brain Sciences, University of Washington, Seattle, WA 98195, USA

eResearch Center for Advanced Technologies, Tokyo Denki University, Inzai, Chiba, JapanfElekta Neuromag Oy, Helsinki, Finland

Received 10 December 2003; revised 11 June 2004; accepted 30 June 2004

Available online 15 September 2004

Abstract

Magnetoencephalography (MEG) detects the brain’s magnetic fields as generated by neuronal electric currents arising from synaptic ion

flow. It is noninvasive, has excellent temporal resolution, and it can localize neuronal activity with good precision. For these reasons, many

scientists interested in the localization of brain functions have turned to MEG. The technique, however, is not without its drawbacks. Those

reluctant to employ it cite its relative awkwardness among pediatric populations because MEG requires subjects to be fairly still during

experiments. Due to these methodological challenges, infant MEG studies are not commonly pursued. In the present study, MEG was

employed to study auditory discrimination in infants. We had two goals: first, to determine whether reliable results could be obtained from

infants despite their movements; and second, to improve MEG data analysis methods. To get more reliable results from infants we employed

novel hardware (real-time head-position tracking system) and software (signal space separation method, SSS) solutions to better deal with

noise and movement. With these solutions, the location and orientation of the head can be tracked in real time and we were able to reduce

noise and artifacts originating outside the helmet significantly. In the present study, these new methods were used to study the biomagnetic

equivalents of event-related potentials (ERPs) in response to duration changes in harmonic tones in sleeping, healthy, full-term newborns.

Our findings indicate that with the use of these new analysis routines, MEG will prove to be a very useful and more accessible experimental

technique among pediatric populations.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Auditory discrimination; Event-related potentials (ERPs); Infants; Magnetoencephalography (MEG)

Introduction

The investigation of auditory discrimination in infants

has largely been done via behavioral methods (for a review,

see Kuhl, 2000; Stockard-Pope, 2001; Werker and Rubel,

1992). More recently, however, direct brain measurements

0014-4886/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.expneurol.2004.06.030

* Corresponding author. Department of Psychology, University of

Miami, PO Box 249229, Coral Gables, FL 33124-0751. Fax: +1 305 284

3402.

E-mail address: [email protected] (M. Cheour).

such as functional magnetic resonance imaging (fMRI)

(Dehaene-Lambertz and Pena, 2001), optical topography

(Pena et al., 2003), and especially electroencephalography

(EEG) (Cheour et al., 1997; Dehaene-Lambertz et al., 2002;

for a review see Cheour et al., 2001) have been employed in

pediatric populations. Unfortunately, each of these methods

has its limitations, due to, for instance, spatial (EEG) or

temporal resolution (fMRI, optical topography). Moreover,

many other factors besides brain functions affect results

obtained by EEG. Extracellular flow, for example, is

affected significantly by the differing electrical conductivity

190 (2004) S44–S51

M. Cheour et al. / Experimental Neurology 190 (2004) S44–S51 S45

properties of the scalp, skull, cerebrospinal fluid, and the

brain itself (Lounasmaa et al., 1996).

Although it has been available for more than 35 years,

having been invented in 1968 (Cohen, 1968), MEG has only

rarely been employed among pediatric populations. MEG is

noninvasive and offers excellent temporal and spatial

resolution, especially when multiple active sources are

compared within a single experimental session. It works

by detecting magnetic fields generated by the brain’s

cellular currents during synchronized neural activity.

Among the few infant MEG experiments is a study by

Paetau et al. (1994) in which data from a single 3-month-old

infant and from a group of older children and adults are

reported. In this study, tones, tone pairs, and pseudowords

were presented to the subjects. The authors reported that all

stimulus types elicited substantially similar responses in all

age groups. However, in the infant, the authors did not see

any N1m response but a large positivity P1m peaking at 190

ms and dominating the response up to 500 ms. Another

example of the rare infant MEG experiments is a study by

Lengle et al. (2001) on twenty 2- to 6-week-old infants.

Authors report that in all neonates tones presented to them

elicited a dominant component that was negative toward the

face and positive toward the occiput. Huotilainen et al.

(2003) also published recently the infant MEG responses

recorded from the infant’s left hemisphere, where they

showed mismatch activity in response to a frequency-

deviated sound.

Recently, a few MEG fetus studies have also been

published. Blum et al. (1985) were the first to demon-

strate that MEG can be used to obtain fetal auditory

evoked responses. Since then, some other researchers

have applied this technique to study fetuses. These studies

have reported that auditory evoked potentials can be

detected in 28–80% of the subjects, depending on, for

example, how many times MEG recordings were

performed for each subject (Eswaran et al., 2002a,b;

Lengle et al., 2001; Schneider et al., 2001; Wakai et al.,

1996; Zappasodi et al., 2001).

Four factors help to explain why infant MEG studies are

still rare. Naturally, the higher price of MEG as compared to

EEG might be one of the reasons why MEG infant studies

are so rare. Second, to obtain reliable results in MEG

experiments, it is important that the subject remains

relatively still. Obviously, this is difficult to achieve in

infants. Third, MEG requires that the infant’s head is close

to the MEG helmet wall to obtain reliable results. This

presents a challenge because existing helmets are designed

for adults, making them too large to offer uncompromised

accuracy in infant studies. Fourth, factors such as the

proximity between the heart and the MEG helmet can

greatly influence the MEG signal. The infant heart is closer

to the head than it is in adults and infant heart beats,

approximately 10 times larger than their brain magnetic

fields at MEG sensor location, largely disturbs MEG

recordings.

In the present study, as the first report of a series of

experiments on neonates, 6-month-olds, and 12-month-olds

using simple tones, complex harmonic tones, and syllables,

we report preliminary results indicating the feasibility of

using MEG to study auditory discrimination in neonates.

Our findings rely on the invention of MEG techniques that

helped us to overcome some of the aforementioned

problems with infants. First, we localized the head position

and orientation during each epoch, making it possible to

average the epochs by selecting those in which the head

position was close enough to a predefined reference

position. Secondly, we employed an algorithm, called the

signal space separation (SSS) method, that greatly reduces

magnetic disturbances that originate outside the multi-

channel sensor helmet by separating the signal space that

produces brain signals (those inside the sensor helmet) from

the disturbance space outside the sensor helmet (Taulu et al.,

in press). The magnetic field distributions over the sensor

volume that are induced by currents from the inside are

fundamentally different from those induced by currents on

the outside, allowing us to effectively reduce magnetic noise

originating from outside the helmet. The combination of

these features made it possible to use the magnetic

responses of infants to examine their abilities to discriminate

auditory signals.

Materials and methods

Subjects

Eight full-term newborns (five females, gestational age

[GA]: 37–42 weeks) participated in this study. Parents

provided written informed consent for their infants to

participate in the experiments. Infants were tested 1–5 days

after birth. During the recordings infants were asleep.

Stimuli

An oddball sequence was employed, including a single

standard (P = 85%) and a single deviant stimulus (P =

15%) that differed in duration; the standard sounds were 100

ms in duration and the deviant sounds were 40 ms. All the

sounds were composed of three sinusoidal partials of three

500-Hz harmonic sounds, that is, 500, 1000, and 1500 Hz.

The constant stimulus-onset asynchrony was 800 ms.

Stimulus sequences were pseudo-random and each deviant

tone was preceded by at least two standard tones. The

stimuli were delivered via nonmagnetic headphones to the

right ear of each subject. The duration of the experiment was

about half an hour.

Recording

All experiments were conducted in a magnetically

shielded room of the BioMag Laboratory of the Helsinki

M. Cheour et al. / Experimental Neurology 190 (2004) S44–S51S46

University Central Hospital. Although the helmet geometry

of whole-head 306-channel MEG system (Elekta Neuro-

magR, Elekta Neuromag Oy, Helsinki, Finland) is designed

for adult measurements, this system was employed to record

the infant’s auditory magnetic responses from their left

hemisphere. In the Elekta NeuromagR system, the magnetic

field is sampled with 510 superconducting sensor loops at

distinct locations. One hundred two loops are used as

magnetometers and the remaining 408 are hardwired to

form 204 planar gradiometers. The bases of the gradiom-

eters are orthogonal and their midpoints coincide. First, the

planar gradiometers measure two orthogonal spatial deriv-

atives of Bz (BBz/Bx and BBz/By); Bz is the radial

component of the brain’s magnetic field B. For idealized

dipolar sources, this planar-type gradiometer shows the

maximum amplitude right above a local current source in

the brain. The baseline length of each planar gradiometer is

16.5 mm and the size of this pickup coil is 2 � 10 � 28 mm.

Second, a single magnetometer records Bz. As a result, the

spatial distributions of Bz, BBz/Bx, and BBz/By are recorded.

Average physical center-to-center distance between sensors

is 35.7 F 1.1 mm, which corresponds to effective channel

separation at 20 mm (see Ahonen et al., 1993). The origin of

the device coordinate system is at the center of the sensor

helmet with the x-axis directing from left to right channel

and the y-axis from posterior to anterior channel. The left

side of each infant’s head was positioned over a part of the

306-channel sensor array that would correspond to the adult

occipital region.

Epochs of 800-ms duration (including a 100-ms presti-

mulus baseline) were digitized at 600 Hz (analog filter pass

band 0.1–172 Hz). During the recording, each subject’s

head location and orientation with respect to the device’s

coordinate system was recorded (every 167 ms; carrier

frequencies: 154, 158, 162, and 166 Hz) by the continuous

head monitoring system.

Analysis

Because we recorded the head position and orientation

every 167 ms, first we calculated the head position and

orientation of every epoch. Epochs that had the head

location closer than 5 mm to the median head location of

all recorded epochs were accepted to be averaged. There-

fore, epochs that had the head position more than 5-mm far

away from the median location were rejected. Subsequently,

artifacts arising from outside the sensor array, such as those

stemming from infant heart beat, limb movement, or other

ambient magnetic disturbances, were greatly reduced by the

signal space separation method (SSS) (Taulu et al., in press).

This method efficiently separates brain signals from external

disturbances based on fundamental properties of the

magnetic fields.

The SSS method is based on the fact that a modern MEG

device, comprised of more than 300 signal channels,

provides generous spatial oversampling of both biomagnetic

and external disturbance magnetic fields. This is true for all

sources located more than a couple of centimeters from the

nearest sensor in the array, an assumption valid for the

possible magnetic sources in the current MEG devices. As

the spatial complexity of the field decreases as a function of

distance, the number of channels exceeds the essential

number of degrees of freedom of the measured field even for

the nearest sources. Consequently, a relatively low-dimen-

sional magnetic subspace spanning all measurable magnetic

fields can be determined.

Because the sensor array is located in a source-free

volume between the volume of interest and the volume

containing all sources of external interference, it turns out

that the magnetic subspace can be split into two separate,

linearly independent subspaces: one containing signals of

interest to us coming from the volume surrounded by the

sensors and another containing signals from sources outside

the array. As the former volume contains the biomagnetic

signal sources and the latter volume contains the external

disturbance sources, any measured signal can be uniquely

decomposed into the magnetic subspace with separate

coefficients corresponding to the subspace spanning the

biomagnetic signals and to the subspace spanning the

external disturbance signals.

The basic assumption of having a source-free sensor

volume means that the magnetic field b can be expressed as

a gradient of a harmonic scalar potential V in that volume.

The harmonicity of the potential allows one to express V as

an expansion of harmonic basis functions, for example,

spherical harmonic functions. Furthermore, this expansion

can be separated into two different expansions; one for the

sources closer to the center of the expansion than any of the

sensors and another for sources more distant to the center of

the expansion than any of the sensors. Consequently,

different basis signal vectors can be determined for signals

produced by internal and external sources. In this way, the

linearly independent SSS basis is formed for modern

multichannel measurement devices with a sufficiently high

number of channels allowing one to elegantly reduce the

external interference signals.

After completing the SSS process, the averaged data

were digitally lowpass filtered (cutoff, 20 Hz; decay, 48 dB/

oct), and then the linear trend was eliminated by removing

the DC components of the 100-ms prestimulus and the 100-

ms tail baselines; the 100-ms tail baseline is equivalent to

the 100-ms prestimulus baseline of the next epoch.

Because the head location and orientation determined by

the median location during each subject’s measurement

were not equivalent across subjects, each subject’s original

head coordinate system was translated and rotated to

correspond to a reference or standard head location; the

head coordinate system has its x-axis directing from the left

to right pre-auricular point and its y-axis from the origin to

the nasion. To convert into the standard head location, each

subject’s head was virtually moved as follows. First, the

center of a sphere approximated to each subject’s brain was


moved to the point (0.0 �30.0 10.0) [mm] of the device

coordinate system. Second, each subject’s head was rotated

so that all three axes of its head coordinate system ran

parallel to the axes of the device coordinate system (see Fig.

1). We assumed that the center of the approximated sphere

of the newborn infant’s brain was at (0.0 4.7 36.6) [mm]

with respect to the head coordinate system and its radius

was 50.0 [mm]; these specific values were obtained by

reducing the adults’ average brain size and approximated

sphere. The magnetic data at each channel were converted

using minimum norm estimates (Uutela et al., 2001) as if the

infant’s head position had been at the standard head position

and orientation.

The difference waveforms were obtained by subtracting

the averaged response to repetitively presented standard

stimuli from the response to occasionally presented deviant

stimuli. After the head position standardization, the follow-

ing two analyses were performed.

The magnetic amplitude of the gradiometer was calcu-

lated at 10 channel locations that have distances less than 65

Fig. 1. Effect of SSS algorithm. In the left panel, responses of subject SH to t

hemisphere after lowpass filter (cutoff: 40 Hz) processing and a baseline (�100 to

described in the inset. Waveforms in triple sensor 25 are enlarged on the right pan

waveforms after applying the SSS algorithm. In the right panel, the left upper fram

lower frame shows the waveforms recorded by an x-axis-oriented gradiometer, an

directions of x-axis and y-axis, shown in the inset, are based on the channel coordi

the channel location and direction on the sensor helmet. A filled green circle ind

leftmost point of the approximated sphere, in the original measurement location. A

head position standardization; in Figs. 2 and 3, waveforms, contour maps, and

Materials and methods section.

mm to the leftmost point of approximated sphere, which

was (�50.0 4.7 36.6) [mm] with respect to the head

coordinates (see Fig. 2). Because we do not have each

subject’s magnetic resonance images, this leftmost point is

supposed, based on adult brain structure, to be located at the

leftmost point of the left temporal region along or close to

the Sylvian fissure, which is the auditory region. To obtain a

good S/N ratio comparable to the adult measurement, the

reference distance 65 mm was determined in such a way that

95% of the Elekta NeuromagR channels are closer than this

distance to the approximated sphere when the adult sits in

the average position (calculated from 206 experiments). If

the distance is longer, the signal from the brain is very small,

resulting in worse S/N ratio. The waveforms from the

gradiometers are analyzed because they show a maximum

peak right above the neuronal current (good spatial

correspondence) and also show an approximate direction

of the neuronal current beneath that channel. We mainly

analyzed the difference waveforms. To find the peak

amplitudes and latencies of the difference waveforms, we

he deviant stimulus are shown for 18 channel locations covering the left

0 ms) correction. The location of brain relative to the channel locations is

el, where thin red curves are original waveforms and thick blue curves are

e shows the waveforms recorded by a y-axis-oriented gradiometer, the left

d the right frame shows the waveforms recorded by a magnetometer. The

nate system, and therefore the absolute directions are different depending on

icates the channel closest to a point (�50.0 4.7 36.6) [mm], which is the

n open green circle shows the location closest to the above point after the

arrow maps are always illustrated after head position standardization. See

Fig. 2. Waveforms from individuals. Difference waveforms of the four individual subjects. Channels in the occipital area of an adult-size sensor helmet were

used in the actual recording of magnetic responses from the neonates’ left hemisphere, but the waveforms here were shown after the head position

standardization. The channels, numbered from 1 to 10, are 10 channel locations closest to the leftmost point of the approximated sphere (�50.0 4.7 36.6) [mm]

in a shorter distance order.


divided the latency range into three sections based on visual

inspection of the data and previous studies (compare Cheour

et al., 1998); the first range from 40 to 100 ms correspond-

ing to event-related potential’s (ERP’s) early component

(EC), the second one from 200 to 300 ms corresponding to

ERP’s mismatch negativity (MMN), and the third one from

350 to 550 ms corresponding to ERP’s late discriminative

negativity (LDN). We identified the peaks separately

depending on the current orientation in terms of ERP

measurement, namely positive and negative current, where

the negative current flows through the auditory region from

the superior to inferior direction, resulting in a vertex

negativity in an EEG recording. For further analysis, we

employed the peak whose amplitude has the S/N of more

than 2. The noise level was the standard deviation of the

first baseline (�100 to 0 ms). The amplitude refers to the

vector sum of a pair of orthogonal gradiometer outputs.

Results

Using the real-time head-position tracking system, we

could effectively reject the epochs in which the head

position was farther than 5 mm from the reference head

position. The SSS algorithm reduces noise components from

sources originating outside the sensor helmet (Taulu et al.,

in press). Fig. 1 illustrates the effects on gradio- and

magnetometer channels in a representative infant. The inset

of Fig. 1 shows a sketch of the brain to show its

approximate location relative to the sensor channels after

head position standardization.

After the SSS process, large artifacts still remained in

four out of eight subjects. We regarded that the data were

contaminated by the large artifact when the maximum

waveform peak, calculated from the waveforms digitally

filtered at 20 Hz and with removal of the DC component

during the two baselines, resides outside the latency range

from 60 to 700 ms. These data were eliminated from further

analysis, resulting in a final analysis of MEG data from four

females (age: 2–5 days, GA: 38–42 weeks). Fig. 2 shows

the difference waveforms obtained from four subjects at 10

triple sensor locations (20 gradiometers and 10 magneto-

meters), covering most of the left hemisphere. Large

deflections are seen mainly in the channels close to the left

auditory region.

As indicated in the Materials and methods section, the

difference waveforms at the 10 triple sensor locations shown

in Fig. 2 were analyzed in two ways after head position

standardization. Table 1 shows the significant negative and

positive peak data for the three latency ranges. In the first

latency range (40–100 ms), one subject showed a negative

peak and two subjects showed a positive peak. In the second

latency range (200–300 ms), one subject showed a negative

peak and three subjects showed a positive peak. In the third

Table 1

Individual peak data

Subject Negative peak Positive peak

Latencya Amplitudeb S/N Sensor Latencya Amplitudeb S/N Senso

(a) Latency range: 40–100 ms

ah 56.61 45.95 2.93 6

sh

ch 84.91 60.75 4.27 2

bh 61.6 30.16 4.85 6

(b) Latency range: 200–300 ms

ah 276.38 86.42 5.51 6

sh 234.76 35.97 10.19 7

ch 259.73 80.74 5.15 3

bh 229.76 53.21 8.55 6

(c) Latency range: 350–550 ms

ah 382.94 110.64 7.06 6

sh 431.22 118.1 5.35 9

ch 497.82 105.7 6.74 3

bh 454.53 39.04 9.07 3

Each subject’s waveform peak amplitude, latency, S/N (amplitude to baseline noise) ratio, and the sensors that show those peaks are shown separately in each

of three latency ranges: (a) 40–100 ms, (b) 200–300 ms, and (c) 350–550 ms. bNegative peakQ corresponds to the peak that shows a negative current from

superior to inferior region. bPositive peakQ refers to the peak that has a positive current from inferior to superior region.a ms.b fT/cm.


latency range (350–550 ms), two subjects showed a

negative peak and two subjects showed a positive peak.

Except for one case in the first latency range, all significant

peaks have the S/N of more than 4.

Fig. 3 shows each individual’s contour maps and arrow

maps at the maximum peak latencies (Table 1) in each of

three latency ranges. The arrow maps show the approximate

current flow (direction and relative magnitude) beneath each

channel location.

Discussion

Our results demonstrate that MEG is a promising tool for

recording auditory ERP in infants. The real-time head-

position tracking system enabled us to average the epochs

by rejecting the epochs in which head location deviated

significantly from the reference head position. Moreover,

the new algorithm reduced the magnetic interference from

outside the sensor helmet in our newborn baby recording as

well.

The waveform peak analysis showed that at the first

latency range (40–100 ms) there was no clear consistent

tendency across subjects but one subject showed a negative

peak and two subjects showed a positive peak. The second

latency range (200–300ms) showed a trend of a positive peak

although one subject showed a negative peak. The third

latency range (350–550 ms) did not show a consistent

tendency across subjects either because two subjects showed

a negative peak and two subjects showed a positive peak.

Although we could not find a consistent magnetic

counterpart of ERP’s early component, two subjects showed

r

a magnetic peak corresponding to the positive current. In

some previous EEG studies, early negativity has been

reported instead of positivity. This has typically not been

very large in amplitude and, according to some studies,

seems to appear so quickly after the stimulus presentation

that it may include some middle-latency components

(Cheour et al., 1997; Ceponiene et al., 2002; Pihko et al.,

1999). If this is the case, it is unlikely that MEG would

detect these deep sources. One also needs to take into

account that due to individual variation in ERP latencies in

infants, it is likely that in the figures presenting group data

this EC often seems to be overlapped by the next

component, MMN (Ceponiene et al., 2002).

The magnetic pattern of a positive current continued from

the first latency range to the second latency range; thus, from

40 to 300 ms. In the second latency range (200–300 ms), a

significant peak was detected in all subjects. In three out of

four subjects, it showed a magnetic deflection corresponding

to a positive current and in one subject it showed a magnetic

deflection corresponding to a negative current.

Previous infant EEG findings in this latency range have

been contradictory. Some studies have reported only

negativities (Cheour et al., 1997; 2002a,b; 2003; Tanaka et

al., 2001) and some only positive responses (Dehaene-

Lambertz et al., 2002; Pihko et al., 1999; Winkler et al.,

2003). The majority of them, however, have reported both

positive and negative responses (Ceponiene et al., 2002;

Kushnerenko et al., 2002; Kurtzberg et al., 1995; Leppanen

et al., 1997; Morr et al., 2002).

In the third latency range (350–550 ms), a significant

peak was detected in all subjects; two out of four subjects

showed a magnetic deflection corresponding to a positive

Fig. 3. Contour maps and arrow maps from each individual. Contour maps and corresponding arrow maps at the peak latencies in each latency range. In the

contour maps, the magnetic flux comes out of the head in the red line regions and goes into the head in the blue line regions. In the arrow maps, the arrow

direction shows the current direction below each sensor and the arrow size relatively corresponds to the current magnitude. The arrow size is consistent only

within a subject.


current and the others showed a magnetic deflection

corresponding to a negative current. Most EEG studies

have reported a large amplitude negative response called

late discriminative negativity (LDN) at this latency range

(Ceponiene et al., 2002; Korpilahti et al., 1995; Martynova

et al., 2003).

Our results show that individual variations seem to be

quite large in infant MEG data. This finding is consistent

with infant EEG studies. Several explanations have been put

forth. Some authors have suggested that infant hearing is

quite immature and it is likely that not all infants are able to

accurately discriminate sounds. This may very well be the

case, especially if simple tones are used, because recent

studies have shown that the infant auditory cortex is, indeed,

quite immature (Moore, 2002).

It has been further proposed that some of the components

may overlap. It has been debated whether large positive

responses commonly reported at the latency of 200–350 ms

reflect a drift in attention, and whether they obscure an

underlying negative component (Ceponiene et al., 2002;

Kushnerenko et al., 2002). Although it seems unlikely that

this positive response solely reflects orientation toward the

stimuli and therefore is an infant version of the P3a

component, it seems nonetheless likely that there are two

different components appearing in infant ERPs at the

latency range of 200–300 ms. Unfortunately, this debate

has thus far been futile because EEG offers only limited

capacity to separate two different components. MEG has the

potential to offer, for the first time, the genuine possibility of

separating infant ERP components that have different scalp

distributions.

The present study demonstrates a substantial methodo-

logical improvement in MEG data collection and data

analysis. Disturbances due to the heart and limbs remain a

problem in some infants. In addition, adult-sized helmets are

too large for infants; to improve data quality, infant-sized

helmets should be designed. In spite of these limitations,

however, the present study demonstrates that MEG is a

viable tool for research on infant auditory processing. As

such, MEG will be a valuable asset for future investigations

of infants’ perception of complex auditory stimuli such as

speech.


Acknowledgments

The authors would like to thank Jussi Nurminen for his

assistance in data collection and Dr. Irving Smith for his

comments on the manuscript. We would also like to thank

the BioMag laboratory of the Helsinki University Central

Hospital for allowing us to collect the data in their

laboratory. Funding for the research was provided by grants

to PKK (NIH HD37954), the Human Frontiers Science

Program (HF159), and the William P. and Ruth Gerberding

University Professorship. Additional support provided by

the Talaris Research Institute, the family Foundation of

Bruce and Jolene McCaw, and the Institute for Learning and

Brain Sciences is gratefully acknowledged.

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magnetoencephalography is feasible for infant assessment...

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