14984 • The Journal of Neuroscience, October 19, 2011 • 31(42):14984 –14988

Brief Communications

Lateralized Theta Wave Connectivity and Language
Performance in 2- to 5-Year-Old Children
Mitsuru Kikuchi,1,2 Kiyomi Shitamichi,2 Yuko Yoshimura,2,3 Sanae Ueno,2 Gerard B. Remijn,4 Tetsu Hirosawa,2Toshio Munesue,1 Tsunehisa Tsubokawa,5 Yasuhiro Haruta,6 Manabu Oi,1 Haruhiro Higashida,1 and Yoshio Minabe2
1

Research Center for Child Mental Development, and 2Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa
University, Kanazawa 920-8641, Japan, 3Higher Brain Functions and Autism Research, Department of Child Development, United Graduate School of Child
Development, Kanazawa University and Hamamatsu University School of Medicine, Osaka University, Osaka 565-0871, Japan, 4International Education
Center, Kyushu University, Fukuoka, Japan 815-8545, 5Department of Anesthesiology, Graduate School of Medical Science, Kanazawa University,
Kanazawa 920-8641, Japan, and 6Department of MEG, Yokogawa Electric Corporation, Tokyo 180-8750, Japan

Recent neuroimaging studies support the view that a left-lateralized brain network is crucial for language development in children.
However, no previous studies have demonstrated a clear link between lateralized brain functional network and language performance inpreschool children. Magnetoencephalography (MEG) is a noninvasive brain imaging technique and is a practical neuroimaging method
for use in young children. MEG produces a reference-free signal, and is therefore an ideal tool to compute coherence between two distantcortical rhythms. In the present study, using a custom child-sized MEG system, we investigated brain networks while 78 right-handed
preschool human children (32– 64 months; 96% were 3-4 years old) listened to stories with moving images. The results indicated that left
dominance of parietotemporal coherence in theta band activity (6-8 Hz) was specifically correlated with higher performance of languagerelated tasks, whereas this laterality was not correlated with nonverbal cognitive performance, chronological age, or head circumference.
Power analyses did not reveal any specific frequencies that contributed to higher language performance. Our results suggest that it is not
the left dominance in theta oscillation per se, but the left-dominant phase-locked connectivity via theta oscillation that contributes to thedevelopment of language ability in young children.

Introduction
One of the most essential features of the human brain is theasymmetrical development of language functions between thetwo hemispheres. Even in the prenatal brain, anatomical asymmetries in language-related brain areas emerge as a function ofbrain development (Wada et al., 1975; Chi et al., 1977), suggesting that the bias for left hemisphere language lateralization ispresent quite early in development. Over the past decade, researchers have focused on how functional asymmetries relate tolanguage development. Some studies have demonstrated a plausible link between brain functional asymmetries and language
development in young children. Functional magnetic resonanceimaging (fMRI) studies have demonstrated that left hemisphere
dominance of language function may already be present in infants ⬍1 year old (Dehaene-Lambertz et al., 2002; Peña et al.,2003). In older children (⬎5 years old), not only left-lateralized

Received June 4, 2011; revised July 30, 2011; accepted Aug. 22, 2011.
Author contributions: M.K., H.H., and Y.M. designed research; M.K., K.S., Y.Y., S.U., G.B.R., T.M., T.T., and Y.M.
performed research; M.K., K.S., Y.Y., S.U., G.B.R., T.H., Y.H., and M.O. analyzed data; M.K. wrote the paper.
This work was supported by the Hokuriku Innovation Cluster for Health Science (MEXT Program for Fostering
Regional Innovation). We thank Gen Uehara (Kanazawa Institute of Technology) and Susumu Matsukura (Yokogawa
Electric Corporation) for their technical support.
This article is freely available online through the J Neurosci Open Choice option.
Correspondence should be addressed to Mitsuru Kikuchi, Research Center for Child Mental Development,
Kanazawa University, 13-1 Takara-machi, Kanazawa 920-8641, Japan. E-mail: mitsuru@zc4.so-net.ne.jp.
DOI:10.1523/JNEUROSCI.2785-11.2011
Copyright © 2011 the authors 0270-6474/11/3114984-05$15.00/0

responses during language-related tasks (Balsamo et al., 2002;
Ahmad et al., 2003), but also correlations between brain functional lateralization and language performance (Everts et al.,2009) have been reported. Previous fMRI studies, however, did
not concentrate on the link between language performance andbrain functional lateralization in healthy preschool children aged
1-4 years old, because this is a challenging age for brain imaging
methods such as fMRI.
Coherent brain rhythms represent a core mechanism for
sculpting temporal coordination of neural activity in the brainwide network (Wang, 2010). Brain rhythmic activity in one area
is likely to be communicated to another and so generate temporal
linkage in the rhythmicity in the two structures. For example,
hippocampal theta rhythm is believed to play a crucial role in theformation and retrieval of episodic and spatial memory (Hasselmo, 2005), and theta rhythm was found to be enhanced invarious neocortical sites during working memory in humans(Raghavachari et al., 2001; Meltzer et al., 2008). Coherent oscillations are thought to play a role in well timed coordination andcommunication between neural populations simultaneously engaged in cognitive processes (Wang, 2010). In the present study,
we hypothesized that left lateralization of neuronal network connectivity is correlated with higher language performance in preschool children. For analysis of cortical network connectivity in
2- to 5-year-old children, we performed coherence analysis using
a custom child-sized magnetoencephalogram (MEG) system (Kikuchi et al., 2010), because this is a noninvasive technique that

Kikuchi et al. • Brain Connectivity and Language in Young Children

J. Neurosci., October 19, 2011 • 31(42):14984 –14988 • 14985

fort during measurements. Parents agreed totheir child’s participation in the study with fullknowledge of the experimental nature of theresearch. Written informed consent was obtained before enrolment in the study. The Ethics Committee of Kanazawa University
Hospital approved the methods and procedures, all of which were in accordance with the
Declaration of Helsinki.
The Kaufman Assessment Battery for Children was applied in all children. The meanscore on the mental processing scale was
99.90 ⫾ 13.50 (mean ⫾ SD) in all participants(n ⫽ 78), and the mean score on the achievement scale was 103.79 ⫾ 16.16 in 76 participants (two participants were not available forassessment on the achievement scale because
they were younger than 36 months). As wewere especially interested in the relation between brain lateralization and development oflanguage performance in children, languagerelated cognitive performance was calculated
by adding the scores of the Expressive Vocabulary and Riddles subtests. In addition, nonverbal cognitive performance was calculated byadding the scores of the Face Recognition and
Hand Movement subtests (Kaufman and
Kaufman, 1983).
Figure 1. A, In the custom child-sized MEG system, the MEG sensors are as close to the whole head as possible for optimal
MEG data were recorded with a 151-channel
recording in young children. During MEG recording, the children lay supine on the bed and viewed video programs projected onto superconducting quantum interference device
a screen. B, Distances between MEG sensors (temporal area) and hippocampus. In 3- to 4-year-old children (left), sensors were (SQUID) whole-head coaxial gradiometer
close enough to the parahippocampal gyrus to record its activity, whereas in a conventional MEG system (right), the distance is too MEG system for children (PQ 1151R; Yokgreat to obtain a good signal from the parahippocampal gyrus. Open circles indicate MEG SQUID sensors (rear view). C, Schema of ogawa/KIT) in a magnetically shielded room
five selected sensors and 10 connections of interest (solid line) in each hemisphere.
(Daido Steel). MEG data were acquired with asampling rate of 1000 Hz and filtered with a
200 Hz low-pass filter. One MEG session lasted
provides measures of cortical neural activity on a millisecondfor 6 min. The experiment consisted of one to eight sessions with ⬃5 min
breaks in between. The number of sessions depended on the participant’s
time scale. Assessment of coherence can determine the degree ofwill (i.e., we finished experiments when participants did not want tophase locking between activities recorded at different sensors,
continue). In the present study, we used data from the first session fromwhich have been used in clinical EEG studies (Wada et al., 1998;
each participant. During MEG recording, children lay supine on the bed
Kikuchi et al., 2002; Higashima et al., 2007). MEG produces aand viewed a video program projected onto a screen. We confirmed thereference-free signal, and is therefore an ideal tool to compute
position of the head to be in the center of the MEG helmet by measuringcoherence between two distant cortical rhythms. In addition,
the magnetic fields after passing currents through coils that were attachedmagnetic fields generated in the temporal lobe (e.g., auditory
at three locations on the head surface as fiduciary points with respect tostimuli) tend to be reflected to sensors in the ipsilateral hemithe landmarks (bilateral mastoid processes and nasion). Before recordsphere, whereas electric potentials recorded by scalp EEG tend toing, we prepared a number of video programs with stories especiallyspread to both hemispheres. This spatial property of MEG is
attractive to young children. The video program was selected according
particular advantageous in studies investigating the laterality ofto the preference of each participant. Sound of the narrations was carried
to participants binaurally through a tube in front of the subject fromthe cortical oscillations.
speakers placed outside the shielded room. Before recording, we ensured
Materials and Methods
that they were content with the video program selected.
Off-line analysis of the MEG data was performed with Brain Vision
Seventy-eight right-handed children (36 boys, 42 girls) participated inanalyzer (Brain Products) and Matlab (MathWorks). MEG data werethe experiment. The participants had a mean age of 49.6 months (32-64).
resampled at 500 Hz. Data were segmented for 2.5 s. Selection of artifactAll subjects were native Japanese and had no previous or existing develfree segments was based on visual inspection. The process of eliminating
opmental, learning, or behavioral problems according to questionnaire
contaminated data was performed blind to personal data. At least 15
information from their parents. All children participated in cognitive
artifact-free segments (35 s period) were accepted for each subject. MEG
tasks and MEG measurements on two separate days. On both testing
spectra were calculated with the fast Fourier transform with a spectraldays, female staff members made contact with the children and played
resolution of 0.244 Hz. In this study, 10 intrahemispheric coherences forwith them along with the parent(s)/caretaker(s). During MEG recording,each hemisphere were measured between the following five regions of
one staff member (author Y.Y.) escorted each participant into the shieldedinterest: frontal (F), central (C), temporal (T), parietal (P), and occipitalroom, which was decorated with colorful pictures of Japanese cartoon(O) areas (Fig. 1C). Before the off-line analysis, selection of sensors corcharacters and mimicked an attractive vehicle adopted from an animaresponding to these brain areas was done based on the following algotion series popular with preschool children. During measurements, therithm. First, using MRI brain anatomical images in three young children,
staff member stayed in the shielded room, comforting and encouraging
we calculated the distance between sensor and hippocampus or posterior
each participant to maintain a steady bodily position when necessary.
cingulate cortex (PCC) for 151 sensors. Then, in each hemisphere, weParent(s)/caretaker(s) could observe their child during measurements
identified sensors of nearest distance to hippocampus or PCC. Thesethrough a TV monitor. According to their evaluation, none of the participants endured high emotional tension or any other kind of discomsensors were located close to the T or P area, respectively. Next, we14986 • J. Neurosci., October 19, 2011 • 31(42):14984 –14988

Kikuchi et al. • Brain Connectivity and Language in Young Children

selected sensors corresponding to F, C, and O areas based on the following requirements: (1) intersensor distances between T–F, T–C, or T–O
were almost the same as those between T–P (i.e., within a range of 100 –120 mm); (2) the sensor corresponding to the F area was selected among
front row sensors of the MEG device; (3) the sensor corresponding to the
C area was selected from a row of sensors midway between the sensors
corresponding to the F and P areas; and (4) the sensor corresponding to
the O area was selected so that the sensor corresponding to the P area was
located in midway between the sensors corresponding to the C and O
areas. Eventually, the sensors corresponding to the F and O areas werelocated close to the dorsal prefrontal area and occipital area, respectively.
Coherence and relative power were banded into the following eightbands: delta (0.7–3.9 Hz), theta-1 (4.2–5.9 Hz), theta-2 (6.4–7.8 Hz), ␣-1(8.3–9.8 Hz), ␣-2 (10.0–12.0 Hz), ␤-1 (12.2–19.8 Hz), ␤-2 (20.0–29.8
Hz), and gamma (30.0–57.9 Hz). Laterality indices (LIs) were calculated
for each coherence and each relative power using the following formula:LI ⫽ (L ⫺ R)/(L ⫹ R), where L and R are the left and right hemispheres,
respectively.
Pearson’s correlation was used to find significant correlations between
physiological measures (i.e., intrahemispheric coherence or relative
power values in both hemispheres and their LIs) and cognitive performance (or chronological age). Our alpha level was adjusted to 0.05/80 ⫽0.0006 (r ⬎ 0.379) for coherence analysis and to 0.05/40 ⫽ 0.0012 (r ⬎0.365) for relative power analysis, because of the multiple comparisons in
10 interhemispheric pairs in coherence analyses (or five hemisphericsensors in power analyses) for the eight frequency bands. As a complementary approach, an alpha level of 0.05 was also used at the risk of a typeI error to explore correlations between physiological measures andlanguage-related cognitive performance.

Results
Intrahemispheric coherence
There were no significant correlations between intrahemisphericcoherences and cognitive performance or chronological age ineither hemisphere (p ⬎ 0.0006; Fig. 2 B, C).
LI of intrahemispheric coherence
There was a significant positive correlation between LI of theta-2
band coherence (parietotemporal network) and language-related
cognitive performance (r ⫽ 0.390, p ⬍ 0.0006) (Figs. 2 A, 3). ThisLI showed no significant correlation with nonverbal cognitive
performance, chronological age (Fig. 3), or head circumference.
There were no significant correlations between LI of any othercoherence and cognitive performance or chronological age. To
evaluate the existence of a possible gender and/or age effect on thesignificant relationship found in LI of theta-2 band coherence(P–T), we used multiple linear regression to predict the LI (i.e.,
dependent variable) using language-related cognitive performance, age, and gender as predictors (i.e., three independent
variables). The significance level was set at p ⬍ 0.05. In the multiple regression model, language-related cognitive performance
was the significant predictor of the LI (n ⫽ 78, ␤ ⫽ 0.362, p ⬍0.05), whereas age (n ⫽ 78, ␤ ⫽ 0.002, p ⬎ 0.05) and gender (n ⫽78, ␤ ⫽ ⫺0.175, p ⬎ 0.05) did not reach statistical significance.
Relative power value
There was a significant negative correlation between relative
power value in delta band in right P area and chronological age(r ⫽ ⫺0.398, p ⬍ 0.0012). There were no significant correlations
between any other relative power value and cognitive performance or chronological age in either hemisphere (p ⬎ 0.0012).
LI of relative power
There were no significant correlations between LI of any region
and cognitive performance, chronological age, or head circumference (p ⬎ 0.0012).

Figure 2. A, Correlation coefficients between laterality index of intrahemispheric coherences and language-related cognitive performance. Note that a significant correlation was
found in parietotemporal connectivity in theta-2 (r ⫽ 0.390, p ⬍ 0.0006). B, C, Correlation
coefficients between intrahemispheric coherences and language-related cognitive performance in the left (B) or right (C) hemisphere. There were no significant correlations between
intrahemispheric coherences and cognitive performance in either hemispheres (p ⱖ 0.0006).
*p ⬍ 0.0006.

If we used an alpha level of 0.05 at the risk of a type I error,
some significant correlations arose between physiological measures and language-related cognitive performance. If we focusedon the theta-2 band oscillation, the following result emerged inaddition to the results above. There were positive correlations in
LIs of coherence (F–P and C–F; r ⫽ 0.295 and 0.269, respectively)
and in the left coherence values (P–T and F–P; r ⫽ 0.295 and0.234, respectively). There was a negative correlation in the right
relative power value (C area; r ⫽ ⫺0.240).

Kikuchi et al. • Brain Connectivity and Language in Young Children

Figure 3. A, B, Scatter plot of laterality index of theta-2 band coherence (parietotemporalconnectivity) and language-related cognitive performance (r ⫽ 0.390, p ⬍ 0.0006; A), andchronological age (r ⫽ 0.175, n.s.; B). E, Boys (n ⫽ 36); F, girls (n ⫽ 42). Solid line, Regression line for boys; broken line; regression line for girls. L, left; R, right; n.s., not significant.

Discussion
In terms of brain functional connectivity in 2- to 5-year-old children, this is the first report demonstrating the advantages ofleft-lateralized connectivity via theta oscillation for higher performance in semantic processing. A recent study demonstrated
this left lateralization in intrahemispheric hemodynamic connectivity using fMRI in older children (5–18 years old)
(Karunanayaka et al., 2007). This was the only previous study
dealing with functional connectivity in children; our results are
consistent with theirs. Furthermore, our results are in agreementwith many recent anatomical studies in healthy adult and childpopulations. Recent anatomical studies using diffusion tensor
MRI demonstrated that left lateralization in the superior longitudinal fasciculus/arcuate fasciculus is correlated with languagerelated performance in both healthy adults (Catani et al., 2007)
and in 5- to 13-year-old children (Lebel and Beaulieu, 2009),
while this language pathway in the left hemisphere appeared to befunctionally insufficient in 5- to 8-year-old children (Brauer etal., 2011). In addition, in healthy infants aged 0 –3 years old, a
recent volumetric study suggested the importance of relationships between progression of myelin deposition in the left hemispheric white matter and children’s vocabulary acquisition (Pujol
et al., 2006). Most of these previous anatomical studies focusedon the auditory-language pathway from Wernicke’s area to
Broca’s area (e.g., superior longitudinal fasciculus/arcuate fasciculus) and supported the view that the left-lateralized brain network is crucial for the acquisition of linguistic abilities not only inadults but also in developing young children. In the present

J. Neurosci., October 19, 2011 • 31(42):14984 –14988 • 14987

study, we demonstrated that left dominance of parietotemporal
coherence in the theta band is correlated with higher performance on language-related tasks, whereas this laterality is notcorrelated with chronological age or head circumference. The
relatively small age range of the children (32-64 months; 96%
were 3-4 years old) in the present study may have contributed toa reduction in the brain connectivity variation due to age differences. We failed to demonstrate a significant result for frontotemporal coherence. This negative result might be attributed tothe position of the sensor corresponding to the frontal area. In thepresent study, to achieve sparse alignment of MEG sensors, thesensor in frontal area was located close to the dorsal prefrontal
area (rather than Broca’s area).
Given that the processing of language information involves
interactions between multiple regions of the brain, maturation of
the long-distance network should be crucial for language development. In fact, slower (theta) oscillation has been suggested to
be involved in long-distance networks associated with top-down
processing (Siegel et al., 2000; von Stein and Sarnthein, 2000; vonStein et al., 2000) or with working memory and attention (Sarnthein et al., 1998). In addition, phasic increases of theta activity
have been reported during language comprehension tasks (Bastiaansen et al., 2005; Bastiaansen and Hagoort, 2006; Hald et al.,2006). Anomalous asymmetry of theta EEG activity during linguistic tasks was reported in the case of developmental dyslexia(children ⬃10 years old) (Spironelli et al., 2008). These previous
studies and our results support the suggestion that brain activities
in language-related brain regions generate left-lateralized temporal linkages via theta oscillations, which contribute to normal
language development.
The sensors (axial gradiometers) of this child-MEG system
originally had the potential to record brain oscillation even fromdeep brain structures (Kimura et al., 2008), and the combinationof young children (i.e., thin skull) and custom-sized MEG system(i.e., close sensors) achieved a shorter distance between sensors
and deeper brain structures, such as the hippocampal formation.
Before this research, using MRI brain images in three young children, we estimated the distance between the sensors selected forthe temporal lobes and hippocampus. The mean distance was64.2 mm for the custom child-sized MEG system, compared with84.5 mm for a conventional MEG system (PQ 1160C; Yokogawa
Electric) (Fig. 1 A, B). As a result, in the medial temporal cortex,
the signal-to-noise ratio in the custom child-sized MEG systemwas ⬃1.8-fold higher. Although caution must be exercised in
drawing any definitive conclusions, because the oscillations ofdeep brain structures cannot be discriminated from those of shallow brain structures in our analysis, there is no doubt that themagnetic signals recorded by the sensors corresponding to temporal areas contained signals from medial temporal structures(e.g., hippocampal formation). Furthermore, a recent study using intracerebral EEG in patients with epilepsy demonstrated thatsuccessful recall in the auditory verbal learning test was associatedwith the enhancement of temporal synchronization of the theta
rhythms within a cerebral network including the hippocampus,
amygdala, and temporal– occipital neocortex (Babiloni et al.,2009). Thus, it is possible that brain rhythmic activities in medial
temporal structures generate temporal linkages over the temporal or parietal neocortex via theta rhythmicity during the learning
process, and that left lateralization in such linkages may contribute to the higher language performances, as observed in the present study.
One critical limitation should be taken into account when
considering the results of coherence analysis, as performed in the14988 • J. Neurosci., October 19, 2011 • 31(42):14984 –14988

present study. Assessment of coherence (i.e., phase correlation
extracted from the Fourier transform) determines the degree ofphase lag consistencies between activities recorded at different
sensors. However, coherence between a pair of sensors can increase not only because there are two distinct sources, providing
activity that has become more phase-locked, but also because asingle source generates a signal that reaches both sensors. In ourstudy, to reduce the latter possibility, we used sparse alignment of
MEG sensors (e.g., the distance between temporal and parietal
regions was 120 mm in each hemisphere). Given that magnetic
field strength diminishes approximately with the square of the
distance from the source, there must have been a strong source
for theta oscillation if only one source influenced the coherence
between the temporal and parietal area. However, in the present
study, power analyses in temporal and parietal areas failed todemonstrate any significant relationships with language-related
cognitive performance. Therefore, it seems plausible that thehigher language performance in children is indeed correlatedwith the left-dominant phase-locking between two distinct brain
regions (i.e., temporal and parietal).

Notes
Supplemental material for this article is available at http://blog-imgs50.fc2.com/m/i/t/mitsurukikuchi/20110730164911833.htm. Table S1shows correlation coefficients between laterality index of intrahemispheric coherences and language-related cognitive performance. Table
S2 shows correlation coefficients between intrahemispheric coherence(in left or right hemisphere) and language-related cognitive performance. Table S3 shows correlation coefficients between laterality index of power value and language-related cognitive performance.
Table S4 shows correlation coefficients between relative power value andlanguage-related cognitive performance. This material has not been peer
reviewed.

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