Author's personal copy
NeuroImage 44 (2009) 1387–1396

Contents lists available at ScienceDirect

NeuroImagej o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g

The cortical dynamics in building syntactic structures of sentences: An MEG study ina minimal-pair paradigm
Kazuki Iijima a,b, Naoki Fukui c, Kuniyoshi L. Sakai a,b,⁎abcDepartment of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Komaba, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
CREST, Japan Science and Technology Corporation, Kawaguchi-shi, Japan
Department of Linguistics, Sophia University, Yotsuya, Tokyo, Japana r t i c l e

i n f oArticle history:
Received 8 July 2008
Revised 30 September 2008
Accepted 28 October 2008
Available online 8 November 2008
Keywords:
MEG
Sentence processing
SyntaxSemantics
Frontal cortex

a b s t r a c t
The importance of abstract syntactic structures and their crucial role in analyzing sentences have long beenemphasized in contemporary linguistics, whereas the linear order model, in which next-coming words in a
sentence are claimed to be predictable based on lexico-semantic association or statistics alone, has also been
proposed and widely assumed. We examined these possibilities with magnetoencephalography (MEG) andmeasured cortical responses to a verb with either object–verb (OV) or subject–verb (SV) sentence structures,
which were tested in a minimal-pair paradigm to compare syntactic and semantic decision tasks. Signiﬁcant
responses to the normal OV sentences were found in the triangular part of the left inferior frontal gyrus (F3t)
at 120–140 ms from the verb onset, which were selective for explicit syntactic processing. The earliest left F3t
responses can thus be regarded as predictive effects for the syntactic information of the next-coming verb,
which cannot be explained by associative memory or statistical factors. Moreover, subsequent responses in
the left insula at 150–170 ms were selective for the processing of the OV sentence structure. On the other
hand, responses in the left mediofrontal and inferior parietal regions at 240–280 ms were related to syntactic
anomaly and verb transitivity, respectively. These results revealed the dynamics of the multiple corticalregions that work in concert to analyze hierarchical syntactic structures and task-related information, further
elucidating the top-down syntactic processing that is crucial during on-line sentence processing.
© 2008 Elsevier Inc. All rights reserved.

Introduction
It has been proposed that syntactic computation, which recursively
embeds phrases within phrases to produce hierarchical sentence
structures, is a critical component of the uniquely human faculty oflanguage (Chomsky, 1995; Hauser et al., 2002). The initial step towardclarifying such formal computation in systems neuroscience would be
distinguishing between syntactic (form) and semantic (content)
processes in the brain. Earlier functional imaging studies reported
the distinction between syntax and semantics in the left frontal
regions (Stromswold et al., 1996; Dapretto and Bookheimer, 1999;
Kang et al., 1999); however, different words were used for twocontrasting conditions, and thus the distinction might be simply
explained by lexical factors. To overcome this problem, we havedeveloped a minimal-pair paradigm, in which the same set of words
was used to make normal and anomalous sentences for eachcondition. Using this paradigm, our functional magnetic resonanceimaging (fMRI) study has clariﬁed that explicit syntactic processing, ascompared with explicit semantic and phonological processing,selectively enhances the activation in the left inferior frontal gyrus⁎ Corresponding author. Fax: +81 3 5454 6261.
E-mail address: sakai@mind.c.u-tokyo.ac.jp (K.L. Sakai).
1053-8119/$ – see front matter © 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.neuroimage.2008.10.041(IFG) (Suzuki and Sakai, 2003). Using transcranial magnetic stimulation (TMS) in the same minimal-pair paradigm, we have also reported
selective priming effects on syntactic decisions when TMS was
administered to the left IFG at 150 ms after the verb onset (Sakai
et al., 2002). These results suggest the critical involvement of the left
IFG in syntactic processing, but more detailed temporal aspects ofsyntactic processing must be further elucidated.
Recent fMRI and magnetoencephalography (MEG) studies havesuggested that the left IFG activation is modulated by various
linguistic factors, including grammaticality (Friederici et al., 2000a),the structure of the relative clause (Stromswold et al., 1996; Indefrey
et al., 2001), and canonicity (Röder et al., 2002; Ben-Shachar et al.,
2004; Bornkessel et al., 2005; Grewe et al., 2006; Kinno et al., 2008).
As a possible common operation among these linguistic computations
that are subserved by the left IFG, we propose here that merging a pairof syntactic objects is most crucial, which is indeed a fundamental
operation for building syntactic structures of a sentence (Chomsky,1995). In the present MEG study, we thus focus on the structure of aminimal sentence, which is formed by merging a single pair of nounand verb. Figs. 1a, b show the basic structures of object–verb (OV) andsubject–verb (SV) sentences we used, respectively. In the OV sentence,a noun phrase (NP) with an accusative case particle (Acc) -o is
combined with a transitive verb (vt) to form a verb phrase (VP). Note

Author's personal copy
1388

K. Iijima et al. / NeuroImage 44 (2009) 1387–1396

Fig. 1. A minimal-pair paradigm with a minimum sentence consisting of a noun phrase and a verb. A pair of sentences including an object–verb (OV) sentence (‘ude-o ag-e-ru’) (a) anda subject–verb (SV) sentence (‘ude-ga ag-ar-u’) (b) is shown. The same noun was used for both sentences; a transitive verb (vt) and an intransitive verb (vi) were morphologicallyrelated in a pair (Table 1). For both sentence structures, a sentence is divided into a subject (OV: pronominal element, pro; SV: a noun phrase, NP) and a predicate (verb phrase, VP).
The VP is further divided into an NP and V under the OV sentence condition, leading to a more complex structure than the structure under the SV sentence condition. (c) Single trial ofa task. All tasks used the same set of visual stimuli, consisting of an NP, which was either O or S, and a V, which was either vt or vi. One kana letter (e.g., ‘u’) was presented after a V toinform participants to initiate a response. For the explanation of a syntactic decision (Syn) task and a semantic decision (Sem) task, see Table 2.

that Japanese is a verb-ﬁnal language, and that the phonetically nullsubject (pro-drop) is allowed in Japanese, as well as in Spanish and
Italian (Jaeggli, 1981). As shown in Fig. 1a, the presence of an empty
category (EC) has been proposed as a pronominal element (pro)(Chomsky, 1981), which is combined with a VP to form a wholesentence (Saito and Fukui, 1998). In the SV sentence, in contrast, an NP
with a nominative case particle (Nom) -ga is combined with a VP, andindirectly with an intransitive verb (vi), to form a whole sentence(Fig. 1b). The following examples clarify the distinction between thesebasic structures:a) Mary will raise her hand, and John will do so,b) Mary will rise, and John will do so,
as ‘do so’ substitutes for the entire VP in both sentences.
The distinction between vt and vi, i.e., verb transitivity, is one of
the universal aspects of syntactic features, present in English,
Japanese, and other natural languages. In Japanese, there are anumber of morphologically related vt–vi pairs (e.g., ‘ag-e-ru’ and ‘agar-u’; Table 1) that are primarily determined by morphosyntax(Shibatani, 1990), similar to the distinction of raise/rise, fell/fall, lay/lie,and set/sit in English. Each pair of OV and SV sentences was preparedwith an identical noun in the present study, in which the verbs werealso semantically related (Table 1). By simply exchanging the verbs
within a vt–vi pair, a minimal pair of syntactically normal (N) andanomalous (A) sentences was produced under each of the OV and SV
sentence conditions (Table 2). This experimental paradigm is one of
the novel merits of the present study.
Based on this minimal-pair paradigm, we tested two main
linguistic tasks (Table 2): a syntactic decision (Syn) task and asemantic decision (Sem) task. In the Syn task, participants judged
whether sentences were syntactically correct or not (Fig. 1c). To solve
the Syn task, the identiﬁcation of vt or vi, as well as the linguisticknowledge of a syntactic relationship between a case particle and averb, was required. Moreover, the Syn task could not be solved on thebasis of the lexico-semantic relationship between a noun and a verb,
because it was always correct for both syntactically normal sentences
and anomalous sentences. For the Sem task, we made semanticallyanomalous sentences by exchanging verbs among the whole set ofsentences. Here we focused on the lexico-semantic relationship(selectional restrictions) between a noun and a verb. For example,‘ude’ (gloss: arm) and ‘ag-e-ru’ (vt, gloss: raise) are semanticallyassociated, whereas ‘ude’ and ‘tam-e-ru’ (vt, gloss: collect) have little
association. In the Sem task, participants judged whether sentenceswere semantically normal or anomalous, while the presented
sentences were always syntactically correct with respect to theusage of vt and vi.

In our paradigm under the OV sentence condition, the preceding
NP case-marked with an Acc predicts the syntactic information of vt
within the VP, because vt is the only possible verb type within the
VP (Fig. 1a). Since the Syn task involved the judgment on a syntactic
relationship between an NP and the next-coming verb, greater
predictive effects for the syntactic information of the next-coming
verb are expected in the Syn task than the Sem task. Under the SV
sentence condition, in contrast, the preceding NP with a Nom speciﬁes
a VP, but not vi itself (Fig. 1b). Thus, the Syn-selective predictive effects
would be more distinct under the OV sentence condition than the SV
sentence condition.
Besides the structural account of sentence processing, an alternative hypothesis is the linear order model for word sequences, which

Table 1A list of 48 normal sentences
Group Object–Verb (OV)
sentence
I

II

III

Subject–Verb (SV)sentence

Noun-Accude-okagi-owaza-oneji-onuno-o
oyu-o
ase-o
ana-o
hada-ouso-okabi-ohara-okizu-ovt
ag-e-ru
kak-e-ru
kim-e-rushim-e-ru
som-e-ru
tam-e-ru
tom-e-ru
um-e-ruar-as-ubar-as-u
hay-as-u
hiy-as-u
huy-as-u

Noun-Nom
ude-gakagi-gawaza-ganeji-ganuno-gaoyu-gaase-gaana-gahada-ga
uso-gakabi-gahara-gakizu-gavi
ag-ar-ukak-ar-u
kim-ar-u
shim-ar-u
som-ar-u
tam-ar-u
tom-ar-u
um-ar-uar-e-ru
bar-e-ruha(y)-e-ru
hi(y)-e-ru
hu(y)-e-ru

ine-onabe-okoe-omaki-okutsu-onetsu-oyuki-oyuka-otsume-ozure-okaji-okar-as-u
kog-as-u
mor-as-u
moy-as-u
nur-as-u
sam-as-u
tok-as-u
yur-as-u
nob-as-u
nao-s-uok-os-uine-ganabe-gakoe-gamaki-gakutsu-ga
netsu-gayuki-gayuka-gatsume-gazure-gakaji-gakar-e-ru
kog-e-ru
mor-e-rumo(y)-e-ru
nur-e-ru
sam-e-ru
tok-e-ru
yur-e-ru
nob-i-ru
nao-r-uok-i-ruTranslation of SV sentence

the arm rises
the lock engages
techniques succeed
the screw gets tightthe cloth gets dyed
hot water collects
sweat ceases
the hole is ﬁlled
someone's skin gets roughthe lie is exposed
mold grows
someone's stomach gets cold
the number of scratches
increases
the rice withers
the pot gets burnt
the voices are heard
ﬁrewood gets burnt
the shoes get wet
the fever wanes
snow melts
the ﬂoor shakes
someone's nails growthe difference is corrected
the ﬁre starts

Morphologically related vt and vi are paired for each row. According to Shibatani (1990),
the verbs are divided into three groups: groups I (-e-ru/-ar-u), II (-as-u/-e-ru), and III(others). There was no signiﬁcant difference regarding the co-occurrence frequency ofadjacent NP and verb between the normal OV and SV sentences, according to either
Google (http://www.google.co.jp/) [t(23) = − 0.37, P = 0.7 (paired t-test)] or Yahoo(http://www.yahoo.co.jp/) [t(23) = 0.91, P = 0.4].

Author's personal copy
K. Iijima et al. / NeuroImage 44 (2009) 1387–1396

StimuliTable 2
Examples of sentences used in a minimal-pair paradigm
Task
Syntactic decisiontask (Syn)Sentence structure
OVSV

Semantic decisiontask (Sem)

OV
SV

1389

Anomaly
Normal (N)Anomalous (A)‘ude-o ag-e-ru’aarm-Acc raise (vt)‘ude-ga ag-ar-u’c
arm-Nom rise (vi)‘ude-o ag-e-ru’
arm-Acc raise (vt)‘ude-ga ag-ar-u’
arm-Nom rise (vi)‘ude-o ag-ar-u’b
arm-Acc rise (vi)‘ude-ga ag-e-ru’darm-Nom raise (vt)‘ude-o tam-e-ru’
arm-Acc collect (vt)‘ude-ga tam-ar-u’
arm-Nom collect (vi)

We designed this minimal-pair paradigm so that anomalous sentences in the Syn task
violated the syntactic relationship between a case particle and a verb, whereas
anomalous sentences in the Sem task were unacceptable regarding the lexico-semantic
relationship between a noun and a verb. The Syn task thus explicitly required syntactic
processing but implicitly involved semantic processing, whereas the Sem task explicitly
required semantic processing but implicitly involved syntactic processing. We did notuse sentences with dual errors, such as ‘ude-o tam-ar-u’ and ‘ude-ga tam-e-ru’. In bothtasks, the accusative (Acc) and nominative (Nom) case particles corresponded to OV and
SV sentence structures, respectively.
asomeone raises one's own arm.
b
The sentence is syntactically incorrect since vi does not take an object, whereas thelexico-semantic relationship between the noun and verb is correct as in the case of thenormal SV sentencec.
cthe arm rises (e.g., while breathing deeply).
d
The sentence is syntactically incorrect because there is a wrong case particle whencompared with the normal OV sentencea. Note, however, that the sentence becomesgrammatical in a rare case when an arm itself can be regarded as an animate subject,e.g.,‘[robotto-no] ude-ga [iwa-o] ag-e-ru’ ([robot's] arm raises [a rock]). Other nouns areclearly inanimate subjects in SV sentences (Table 1).

predicts next-coming words based on lexico-semantic association or
statistics, i.e., transition probabilities between single words in a
sentence (Cleeremans and McClelland, 1991; Elman, 1991). Greater
predictive effects for the lexico-semantic information of the nextcoming verb are expected in the Sem task than the Syn task,
irrespective of sentence structures, because the Sem task required
the linear order processing of associated words. However, a differential effect on the cortical responses between the normal OV and SV
sentences, if any, cannot be explained by such associative memory or
statistical factors alone, because there was no difference between thenormal OV and SV sentences regarding the co-occurrence frequency ofadjacent NP and verb pairs (Table 1). To examine both the syntactic
and semantic predictive effects on the cortical responses to verbs, we
directly compared the Syn and Sem tasks under each of the normal OV
and SV sentence conditions. For this purpose, we focused on thecortical responses to a verb from the verb onset. The interval between
an NP and a verb was varied, so that the responses to verbs were notconfounded with those to NPs (Fig. 1c). A direct comparison of the Syn
and Sem tasks on the normal sentences is also useful for clarifying thepredictive effects independently from syntactic or semantic anomaly.
Materials and methods
Participants
The participants in the present study were 12 native Japanese
speakers. Two participants, whose data contained large amount ofnoise due to eye movement or blinking (noise-free data during −100–
300 ms: 70.3 and 76.2% each for the excluded participants, 80.9–99.8%
for the others), were discarded from the analysis, leaving a total of 10participants (2 females, 19–31 years). The 10 participants showed
right-handedness (laterality quotients: 86–100) as determined by the
Edinburgh inventory (Oldﬁeld, 1971). Informed consent was obtained
from each participant after the nature and possible consequences ofthe studies were explained. Approval for these experiments was
obtained from the institutional review board of the University ofTokyo, Komaba.

Visual stimuli were presented in yellow letters against a dark
background, which were projected from outside of the shield room
onto the translucent screen (within the visual angle of 5.7°). For
ﬁxation, a red cross was always shown at the center of the screen. Eachvisual stimulus was either an NP (a noun and a case particle) or verb
(Fig. 1c), which always consisted of three letters (three moras or
syllables) spelled in kana letters (Japanese phonograms) to ensure a
consistent reading time among words. Each stimulus was presented
for 300 ms, and the interstimulus interval (ISI) between an NP and averb was randomly varied for 400, 500, 600, 700, 800, and 900 ms. One
kana letter was also presented 1000 ms after the verb onset to informparticipants to start pushing one of two buttons according to a task
instruction. The identity of a kana letter is relevant only in a memory(Mem) task, but we presented a kana letter in the other tasks to keep
stimuli identical. The inter-trial interval was randomly varied within
the range of ±10% at 4 s to reduce any periodical noises. Stimulus
presentation and behavioral data collection were controlled using the
LabView software and interface (National Instruments, Austin, TX).
Tasks
Each of the Syn and Sem tasks was performed in a separate MEG
run. In each run of the Syn task, there were 24 trials and 24 different
sentences for each of normal OV, normal SV, syntactically anomalous
OV, and syntactically anomalous SV sentences. In each run of the Semtask, there were 24 trials and 24 different sentences for each of normal
OV, normal SV, semantically anomalous OV, and semanticallyanomalous SV sentences. As shown in Table 2, verb transitivity (vt,vi) was related to both sentence structure (OV, SV) and anomaly (N, A)
in the Syn task, whereas verb transitivity corresponded to sentence
structure alone in the Sem task. In this paper, the normal OV sentence
condition, for example, is denoted as (OV, N, vt). In both of the Syn and
Sem tasks, a kana letter following a verb was chosen randomly fromsix letters of the stimuli in the same trial. The Syn task explicitly
required syntactic processing but implicitly involved semantic
processing, and vice versa in the Sem task.
Two additional tasks regarding the control of reading, evaluation,and memorization processes involved in the Syn and Sem tasks were
tested in separate runs: an evaluation (Eva) task and a Mem task. In
the Eva task, participants judged whether the impression of eachsentence was positive or negative based on pragmatics, while thepresented sentences were always normal in terms of syntax andlexico-semantics. For example, ‘waza-ga kim-ar-u’ (techniques succeed)
is positive, and ‘hada-ga ar-e-ru’ (someone's skin gets rough) is
negative. Correct answers in the Eva task were determined by apilot study performed before the experiments. We used the Eva task
for analyzing reaction times (RTs) and task selectivity of corticalresponses alone. A kana letter was presented in the same manner as in
the Syn and Sem tasks. In each run of the Eva task, there were 24 trials
and 12 different sentences for each of positive OV (a half of the 24
normal OV sentences), negative OV (the other half of the 24 normal OV
sentences), positive SV (a half of the 24 normal SV sentences), andnegative SV (the other half of the 24 normal SV sentences) sentences.
In the Mem task, participants judged whether or not a kana letter
following a verb matched one of the six letters of the normal sentencein the same trial. In contrast to other tasks, the decision in the Mem
task was delayed until the presentation of a kana letter. We used the
Mem task for analyzing the accuracy and task selectivity of corticalresponses alone. In each run of the Mem task, there were 24 trials and
24 different sentences for each of the matched OV, mismatched OV(with sentences identical to those for the matched OV), matched SV,
and mismatched SV (with sentences identical to those for the matchedSV) sentences. For all participants, four runs were tested for each ofthese four tasks, in which the orders of tasks, and sentence structures

Author's personal copy
1390

K. Iijima et al. / NeuroImage 44 (2009) 1387–1396

Fig. 2. The averaged MEG signals for all trials from ten participants, shown for eachsensor. The black bars above the waveforms indicate the time windows, where
signiﬁcant responses were observed in the contrasts shown in Table 3 and Figs. 3–6.

were fully randomized and counterbalanced. Only trials withparticipants' correct responses were used for analyzing RTs.
MEG data acquisition and analyses
The raw MEG data were acquired with a 160-channel whole-headsystem (MEGvision, Yokogawa Electric Corporation, Kanazawa-city,

Japan), and they were digitized with an on-line bandwidth of 0.3 Hz to1000 Hz and a sampling rate of 2000 Hz. Using the BESA 5.1 software(MEGIS Software, Munich, Germany), the MEG signals evoked by averb from −100 to 300 ms were analyzed, where the signals from −100
to 0 ms were used as a baseline (Fig. 2). Only artifact-free trials (peakto-peak amplitude b2500 fT) with participants' correct responses
were averaged for each condition, and the averaged MEG signals were
band-pass ﬁltered in the frequency domain from 2 to 30 Hz toeliminate large eye movement noises. For mapping with theindividual brain, high resolution T1-weighted MR images (repetitiontime, 30 ms; echo time, 8.0 ms; ﬂip angle, 60°; ﬁeld of view,
256 × 256 mm2; resolution, 1 × 1 × 1 mm3) were acquired using a 1.5-T
Scanner (Stratis II, Premium; Hitachi Medical Corporation, Tokyo,
Japan). The sensor positions were coregistered to the MR images byaligning the ﬁve ﬁducial markers with their visible locations on thehead surface, and ﬁnal adjustments were completed by using a leastsquares ﬁt algorithm (MEG Laboratory, Yokogawa Electric Corporation, Kanazawa-city, Japan). Using the BrainVoyager QX software(Brain Innovation, Maastricht, Netherlands), each individual brain was
normalized to the image of the Montreal Neurological Institute (MNI)
standard brain, which was already transformed into the Talairach
space (Talairach and Tournoux, 1988). In order to perform a cortexbased data analysis, the gray and white matter of the transformed
standard brain was segmented, and their boundary was thenFig. 3. Selective responses to the Syn task. (a) The cortical responses to Syn and Sem were compared with a paired t-test under the normal OV sentence condition, and mapped on thetransformed standard brain (Pcorr b 0.05). Note the signiﬁcant responses in the left (L.) F3t. (b) The averaged temporal changes of the current density for the left F3t. The red and bluelines correspond to the current density for Syn, (OV, N, vt) and Sem, (OV, N, vt), respectively. Their SEMs are shown as shaded bands (n = 10). The interval which resulted in signiﬁcantdifferences is shown with a bar. (c) Histograms for the current density (mean ± SEM) under each normal sentence condition for the left F3t. (d) Histograms for the current density,
including the anomalous sentences for Syn and Sem. Filled and open bars denote the current density under the OV and SV sentence conditions, respectively. The solid and dashedlines with asterisks above pairs of bars correspond to the signiﬁcant contrasts used for the statistical parametric maps and other signiﬁcant contrasts (P b 0.05, paired t-test),
respectively.

Author's personal copy
K. Iijima et al. / NeuroImage 44 (2009) 1387–1396

1391

Fig. 4. Cortical responses to sentence structure or verb transitivity. (a, d) The OV and SV sentence conditions were compared within the Syn task (Table 3). Note the signiﬁcant
responses in the left insula and left supramarginal gyrus (SMG). (b, e) The averaged temporal changes of the current density for the left insula and left SMG, respectively. The red andblue lines correspond to the current density for Syn, (OV, N, vt) and (SV, N, vi), respectively. (c, f) Histograms for the current density under each condition are shown for the left insula
and the left SMG, respectively. Filled and open bars denote the current density under the OV and SV sentence conditions, respectively.

partitioned into 3256 cortical patches with a mean distance of 5.5 mm(Kriegeskorte and Goebel, 2001).
For each participant, the MEG signals of each channel wereaveraged for a bin of 20 ms; the time bin was moved in 10 ms steps
over the 100–300 ms period after the presentation of a verb. The
distribution of cortical activation underlying the averaged MEG
signals was modeled with the minimum norm estimates (MNEs) ofcurrents using BESA 5.1. A current dipole was perpendicularly placedat the center of each cortical patch, approximating any spatial

distributions of currents on the cortex without assuming particular
positions of the dipole sources. (Hämäläinen et al., 1993; Dale andSereno, 1993). The current density at each cortical patch was
calculated by dividing the current strength by the mean area of thecortical patches. The MNEs of currents without averaging for a bin of
20 ms were also obtained and shown in Figs. 3–6 as the temporalchanges of the current density.
Across all participants, a paired t-test on the current density was
performed for two contrasting conditions (see below). The statisticalFig. 5. Cortical responses to syntactic anomaly or sentence structure. (a) The anomalous and normal sentence conditions were compared within the Syn task (Table 3). A parasagittal
section (x = −7) is shown for the left anterior cingulate cortex (ACC) and left orbitofrontal cortex (OFC). (b) The averaged temporal changes of the current density for the left ACC. The
red and blue lines correspond to the current density for Syn, (SV, A, vt) and (OV, N, vt), respectively. (c) Histograms for the current density under each condition are shown for the left
ACC; the left OFC showed a similar tendency.

Author's personal copy
1392

K. Iijima et al. / NeuroImage 44 (2009) 1387–1396

Fig. 6. Cortical responses to verb transitivity or syntactic anomaly. (a) The vt and vi sentence conditions were compared within the Syn task (Table 3). Note the signiﬁcant responses in
the left inferior parietal lobule (IPL). (b) The averaged temporal changes of the current density for the left IPL. The red and blue lines correspond to the current density for Syn, (SV, A,vt) and (SV, N, vi), respectively. (c) Histograms for the current density under each condition are shown for the left IPL.

was performed for the cortical patch with a maximum t-value(Table 3). To examine any Sem-selective responses, we also started
with Sem − (Eva + Mem) / 2 (uncorrected P b 0.005), and then performed Sem − Syn (Pcorr b 0.05).
We next focused on three factors included in the Syn task:
sentence structure (OV, SV), syntactic anomaly (N, A), and verb
transitivity (vt, vi). To examine any selective responses to thesefactors, a statistical parametric map (a paired t-test) was obtained bycontrasting the current density under two conditions (Pcorr b 0.05). For
example, with Syn, (OV, N, vt) – (SV, N, vi), we examined the effect ofsentence structure (OV, SV) or verb transitivity (vt, vi), while syntactic
anomaly (N) was held constant (Table 3). Once selective responses
were found at a particular time bin, a two-way rANOVA was
performed for the cortical patch with a maximum t-value. In therANOVA of sentence structure × verb transitivity, the remaining factorof syntactic anomaly (held constant for a paired t-test) corresponds
to an interaction of two main effects of interest (see the Syn task
in Table 2). Similarly, Syn, (SV, A, vt) – (OV, N, vt) and Syn, (SV, A, vt) –(SV, N, vi) were also performed, in which two factors were selected ina cyclic manner (Table 3).

results for each time bin were further corrected for multiple comparisonsacross the whole cortical patches (Pcorr b 0.05), using a permutation
procedure for the current density of two conditions (Karniski et al.,
1994; Pantazis et al., 2005). For example, in the comparison between
the Syn and Sem tasks, the data of all cortical patches were exchanged
between the two tasks in some of the participants. For such a
permutation, a maximum t-value was determined among the corticalpatches. There were 210 =1024 permutations for 10 participants, which
produced a reference distribution of t-values for determining thecorrected P-values. Correction for multiple comparisons using t-values,each of which is a mean difference normalized by a variance, is superior
in sensitivity than that using simple mean differences of the current
density (Nichols and Holmes, 2002). Note that this method requires noassumption of a normal distribution or of the correlation structure of
the data requiring correction (Karniski et al., 1994). The dipoles withstatistical signiﬁcance were identiﬁed, each of which was further
represented by a sphere with a diameter of 6 mm using ImageJ software(http://rsb.info.nih.gov/ij/). Using the MRIcro software (http://www.
mricro.com/), a spatial Gaussian ﬁlter was applied to these spheres (fullwidth of half maximum, 8 mm), which were then superimposed onto
the transformed standard brain as a statistical parametric map of thecerebral cortex.

Results

Procedures of identifying selective responses

Behavioral data

We ﬁrst compared the tasks under the normal sentence conditions,in which identical sentences were presented (Table 2). To examine any
Syn-selective responses, we adopted a two stage procedure with astatistical parametric map (a paired t-test), starting with contrasting
the current density in the Syn task and the two control tasks, i.e.,
Syn − (Eva + Mem) / 2, with a liberal statistical threshold of uncorrected
P b 0.005. To exclude false positive responses, we then focused on
Syn-selective responses, i.e., Syn − Sem, at the level of Pcorr b 0.05.
Once Syn-selective responses were found at a particular time bin, athree-way repeated measures analysis of variance (rANOVA), further
incorporating the factors of sentence structure and anomaly (Table 2),

For each task, behavioral data of accuracy and RTs are shownin Table 4. We focused on the normal sentence conditions, inwhich identical normal sentences were presented. Regarding theaccuracy for normal sentences, a two-way rANOVA [task (Syn, Sem,
Mem) × sentence structure (OV, SV)] showed marginal main effects oftask [F(2, 18) = 3.4, P = 0.055] and sentence structure [F(1, 9) = 4.7,
P = 0.058] with a signiﬁcant interaction [F(3, 27)= 5.8, P = 0.012]. By
analyzing the accuracy data separately for each sentence structure,paired t-tests showed no signiﬁcant difference in accuracy among
the tasks under the normal OV sentence condition (P N 0.5). Under thenormal SV sentence condition, the accuracy of Syn was signiﬁcantly
higher than Sem [t(9) = 2.4, P = 0.040] and Mem [t(9) = 4.7, P = 0.0011],
and that of Sem was also higher than Mem [t(9) = 2.2, P = 0.054].
Regarding the RTs for normal sentences, a two-way rANOVA [task (Syn,Sem, Eva)× sentence structure (OV, SV)] showed a signiﬁcant main effectof task [F(2, 18)= 6.9, P = 0.0060] with neither main effect of sentence
structure [F(1, 9) = 2.3, P = 0.2] nor interaction [F(2, 18)= 2.1, P = 0.1]. The
RTs of Syn were signiﬁcantly shorter than Eva [OV: t(9) = 2.6, P = 0.028;
SV: t(9)= 2.6, P = 0.029]; the RTs of Sem were also signiﬁcantly shorter
than Eva [OV: t(9) = 2.6, P = 0.031; SV: t(9) = 3.1, P = 0.013]. In contrast,
there was no signiﬁcant difference in RTs between Syn and Sem (P N 0.2).

Table 3A list of statistical analyses
Paired t-testrANOVA

FigureSyn − Sem, (OV, N, vt)
Syn, (OV, N, vt) – (SV, N, vi)Syn, (SV, A, vt) – (OV, N, vt)
Syn, (SV, A, vt) – (SV, N, vi)

task × sentence structure × anomaly
sentence structure × verb transitivity
syntactic anomaly × sentence structure
verb transitivity × syntactic anomaly

3
4
5
6

The italicized factors in each condition for a paired t-test are main effects of interest. See
the Materials and methods for each analysis.

Author's personal copy
K. Iijima et al. / NeuroImage 44 (2009) 1387–1396
Table 4Behavioral data for each task
Task

Sentence
structure

Anomaly
Normal (N)Anomalous (A)Syntactic decision task (Syn)OV

92.9 ± 1.4
575 ± 6496.7 ± 1.1
572 ± 70
92.5 ± 2.1
589 ± 67
94.5 ± 1.6
565 ± 69
89.6 ± 2.4
630 ± 63
88.7 ± 2.0
625 ± 67
92.3 ± 1.3
789 ± 33
91.5 ± 1.6
780 ± 32

92.5 ± 1.9
611 ± 63
93.1 ± 1.9
609 ± 6495.7 ± 1.3
601 ± 7195.8 ± 1.2
598 ± 70

SV
Semantic decision task (Sem)OV
SV

Evaluation task (Eva)OV
SVMemory task (Mem)OVSV

Data are shown as mean ± SEM. Upper row, accuracy (%); lower row, RTs (ms).

These behavioral results indicate that the main linguistic tasks of Syn
and Sem were comparable to or easier than the control tasks of Eva andMem. Therefore, selective responses in Syn or Sem, if any, cannot be
explained by task difﬁculty.
We next focused on the effects of sentence structure and syntactic
anomaly within the Syn task (Table 4). Regarding the accuracy of Syn,a two-way rANOVA [sentence structure (OV, SV) × syntactic anomaly(N, A)] showed a signiﬁcant main effect of sentence structure [F(1, 9) =6.1, P = 0.036; SV N OV] and a marginal main effect of syntactic anomaly[F(1, 9) = 4.8, P = 0.057; N NA] with no interaction [F(1, 9) = 3.7, P = 0.09].
Paired t-tests further revealed that the accuracy under the normal SV
sentence condition (SV, N, vi) was signiﬁcantly higher than the otherconditions [(OV, N, vt): t(9) = 3.0, P = 0.015; (OV, A, vi): t(9) = 2.9,
P = 0.016; (SV, A, vt): t(9) = 2.5, P = 0.032], whereas there was no othersigniﬁcant difference in the accuracy (P N 0.5). This result indicates thatthe normal SV sentence condition was the least demanding among thefour conditions. Regarding the RTs of Syn, there was a signiﬁcant main
effect of syntactic anomaly [F(1, 9) = 10, P = 0.011; A N N] with neither
main effect of sentence structure [F(1, 9) = 0.13, P = 0.7] nor interaction[F(1, 9) b 0.1, P N 0.9]. Paired t-tests showed that the RTs under the
anomalous OV sentence condition (OV, A, vi) were signiﬁcantly longer
than the normal sentence conditions [(OV, N, vt): t(9) = 3.3, P = 0.0087;(SV, N, vi): t(9) = 2.5, P = 0.032]; the RTs under the anomalous SV
sentence condition (SV, A, vt) were also signiﬁcantly longer than thenormal sentence conditions [(OV, N, vt): t(9) = 3.0, P = 0.016; (SV, N,vi): t(9) = 2.5, P = 0.036]. These results indicate that the anomalous OV
and SV sentences were more demanding than the normal sentences.
The longer RTs for the anomalous sentences, which are consistent
with our previous studies using the same paradigm (Sakai et al.,
2002; Suzuki and Sakai, 2003), may be due to the reanalysis ofanomalous sentences.
Cortical responses to task
First, we focused on the task effects by comparing the four tasks
under the normal sentence conditions, in which identical sentenceswere presented (Table 2). In order to clarify selective corticalresponses to the explicit syntactic processing, we examined astatistical parametric map with a paired t-test for directly contrasting
the Syn and Sem tasks (Syn − Sem), ﬁrst under the normal OV sentence
condition (OV, N, vt). We found the earliest Syn-selective responses in
the left pars triangularis of the IFG (F3t) [Talairach coordinates, (x, y, z)= (−47, 35, 9); Brodmann's area (BA) 45; Pcorr = 0.025] at 120–140 ms
after the verb onset (Fig 3a). The temporal changes in this region also1393

revealed enhanced Syn-selective responses, which started to rise as
early as 110 ms (Fig. 3b).
Paired t-tests on the current density of this region under thenormal OV sentence condition showed that the responses to Syn
were signiﬁcantly larger than those to Sem [t(9) = 7.5, P b 0.0001], Eva[t(9) = 3.4, P = 0.0083], and Mem [t(9) = 3.2, P = 0.010] (Fig. 3c). On theother hand, there was no signiﬁcant difference among all task pairs
under the normal SV sentence condition (P N 0.1). We further tested
the task effect, additionally incorporating the factors of sentence
structure and anomaly shown in Table 2. A three-way rANOVA [task(Syn, Sem) × sentence structure (OV, SV) × anomaly (N, A)] showed asigniﬁcant main effect of task [F(1, 9) = 7.2, P = 0.025; Syn N Sem] withneither other main effects [sentence structure: F(1, 9)b0.1 P N 0.9;
anomaly: F(1, 9) = 2.5, P = 0.2] nor interactions (P N 0.1) (Fig 3d). Even
if the responses to the normal and anomalous sentences wereaveraged together under the OV sentence condition, the responses to
Syn were signiﬁcantly larger than those to Sem [t(9) = 2.6, P = 0.029].
Moreover, the responses to Sem under the normal OV sentence
condition were signiﬁcantly smaller than those to Syn under bothnormal and anomalous OV sentence conditions (i.e., with vt and vi)[Syn, (OV, N, vt): t(9) = 7.5, P b 0.0001; Syn, (OV, A, vi): t(9) = 2.4,
P = 0.042]. Therefore, the responses of the left F3t were Syn-selective
under the OV sentence condition, irrespective of syntactic anomaly
or verb transitivity.
During the intervals of 100–120 and 140–300 ms, there was no
signiﬁcant Syn-selective response under the normal OV sentence
condition. Regarding the normal SV sentence condition, there was no
signiﬁcant Syn-selective response during the entire searched intervalof 100–300 ms. We also conﬁrmed that there was no signiﬁcant
response in Sem − Syn under both the normal OV and SV sentence
conditions during 100–300 ms. In Fig. 3b, Sem might have enhanced
the responses in the left F3t during 150–200 ms, but neither Sem −(Eva + Mem) / 2 (uncorrected P N 0.08) nor Sem − Syn (Pcorr N 0.17)
reached signiﬁcance under the normal OV sentence condition.
Cortical responses to sentence structure or verb transitivity
Following the elucidation of the Syn-selective responses, we
examined the effect of sentence structure (OV, SV) or verb transitivity(vt, vi), while syntactic anomaly (N) was held constant (Table 3). In
Syn, (OV, N, vt) – (SV, N, vi), we found signiﬁcant responses in the left
insula [(−33, 8, 19); Pcorr = 0.031] at 150–170 ms (Fig. 4a). The
temporal changes in this region showed enhanced responses to thenormal OV sentences, which started to rise around 130 ms (Fig. 4b).
Next we performed a two-way rANOVA [sentence structure × verb
transitivity] on the current density of this region, in which theremaining factor of anomaly corresponded to an interaction (Table 3).
This analysis revealed a signiﬁcant main effect of sentence structure[F(1, 9) = 13, P = 0.0054; OV N SV] with neither main effect of verb
transitivity [F(1, 9) = 4.0, P = 0.08] nor interaction [F(1, 9) = 0.39, P = 0.6]
(Fig. 4c). Paired t-tests showed that the responses to the SV sentenceswith vi were signiﬁcantly smaller than those to the OV sentences[(OV, N, vt): t(9) = 6.7, P b 0.0001; (OV, A, vi): t(9) = 2.6, P = 0.029].
In the same contrast, signiﬁcant responses were also present in
the left supramarginal gyrus (SMG) [(−59, −23, 23); BA 40; Pcorr =0.025] at 190–210 ms (Fig. 4d). The temporal changes in this region
showed enhanced responses to the normal OV sentences, whichstarted to rise around 150 ms (Fig. 4e). A two-way rANOVA on thecurrent density of this region showed neither main effects [sentence
structure: F(1, 9) = 2.5, P = 0.2; verb transitivity: F(1, 9) = 3.0, P = 0.1]
nor interaction [F(1, 9) = 1.0, P = 0.3] (Fig. 4f). Paired t-tests showedthat the responses to the SV sentences with vi were signiﬁcantly
smaller than those to the OV sentences [(OV, N, vt): t(9) = 7.7,
P b 0.0001; (OV, A, vi): t(9) = 2.4, P = 0.041]. During 100–300 ms, we
conﬁrmed that there was no signiﬁcant response in the following
contrasts, in which syntactic anomaly was held constant: Syn, (SV, N,

Author's personal copy
1394

K. Iijima et al. / NeuroImage 44 (2009) 1387–1396vi) – (OV, N, vt); Syn, (OV, A, vi) – (SV, A, vt); and Syn, (SV, A, vt) –(OV, A, vi).
Cortical responses to syntactic anomaly or sentence structure
We next examined the effect of syntactic anomaly (A, N) or
sentence structure (SV, OV), while verb transitivity (vt) was held
constant (Table 3). In Syn, (SV, A, vt) – (OV, N, vt), signiﬁcant
responses were observed in the left anterior cingulate cortex (ACC)[(−7, 41, 4); BA 32; Pcorr = 0.016] and orbitofrontal cortex (OFC) [(−4,56, −9); BA 10; Pcorr = 0.020] at 240–260 ms (Fig. 5a). In Fig. 5b, Syn,
(SV, A, vt) might have also enhanced the responses in the left ACC
during 170–220 ms, but the same contrast did not reach signiﬁcance(Pcorr N 0.19). A two-way rANOVA [syntactic anomaly × sentence
structure] on the current density of the left ACC at 240–260 ms
revealed signiﬁcant main effects of syntactic anomaly [F(1, 9) = 23,
P = 0.0010; A N N] and sentence structure [F(1, 9) = 6.9, P = 0.028;
SV N OV] with no interaction [F(1, 9) = 0.14, P = 0.7] (Fig 5c). Paired
t-tests showed that the responses to the anomalous SV sentenceswere signiﬁcantly larger than those to the normal sentences [(OV,N, vt): t(9) = 8.1, P b 0.0001; (SV, N, vi): t(9) = 4.1, P = 0.0028]. During
100–300 ms, we conﬁrmed that there was no signiﬁcant response in
the following contrasts, in which verb transitivity was held constant:Syn, (OV, N, vt) – (SV, A, vt); Syn, (OV, A, vi) – (SV, N, vi); and Syn,
(SV, N, vi) – (OV, A, vi).

Cortical responses to the Syn task
The direct comparison between the Syn and Sem tasks revealed
that the OV sentences evoked selective responses to explicit syntactic
processing in the left F3t. The syntax-selective activation of theopercular and triangular parts of the left IFG (F3op/F3t), which is aputative grammar center (Sakai, 2005), has been reported by ourprevious study with a minimal-pair paradigm (Suzuki and Sakai,2003), as well as by other studies (Stromswold et al., 1996; Daprettoand Bookheimer, 1999; Kang et al., 1999; Embick et al., 2000;
Hashimoto and Sakai, 2002). The present study further demonstrated
that the responses of the left F3t are selectively modulated by explicit
syntactic processing as early as 120–140 ms. Cortical responses tovisual words in this time window are often regarded as representing a
pre-lexical process, as shown by lexical tasks (Helenius et al., 1998;
Pylkkänen and Marantz, 2003). However, in our paradigm under the
OV sentence condition, the preceding NP with an Acc already speciﬁes
the syntactic information of vt within the VP (Fig. 1a, see theIntroduction). The Syn-selective responses of the left F3t can thus beregarded as predictive effects for the syntactic information of thenext-coming verb. Under the OV sentence condition of our previous
TMS study, we have reported the priming effects on syntactic
decisions, when TMS was administered to the left F3op/F3t 150 ms
after the verb onset (Sakai et al., 2002). The critical spatio-temporalwindow of the TMS study is thus consistent with that of the present
study, namely, the left F3t and 120–140 ms.

Cortical responses to verb transitivity or syntactic anomaly
Cortical responses to sentence structure
Finally, we examined the effect of verb transitivity (vt, vi) or
syntactic anomaly (A, N), while sentence structure (SV) was held
constant (Table 3). In Syn, (SV, A, vt) – (SV, N, vi), signiﬁcant responses
were observed in the left inferior parietal lobule (IPL) [(−20, −60, 45);
BA 7; Pcorr = 0.032] at 260–280 ms (Fig. 6a). The temporal changes inthis region showed distinct differences between two SV sentence
conditions (Fig. 6b). A two-way rANOVA [verb transitivity × syntactic
anomaly] on the current density of this region revealed a signiﬁcantmain effect of verb transitivity [F(1, 9) = 8.7, P = 0.016] with neither
main effect of syntactic anomaly [F(1, 9) = 3.1, P = 0.1] nor interaction [F(1, 9) = 1.3, P = 0.3] (Fig. 6c). Paired t-tests showed that the responses tothe normal sentences with vi were signiﬁcantly smaller than those to
the sentences with vt [(SV, A, vt): t(9) = 6.5, P = 0.0001; (OV, N, vt): t(9)= 3.0, P = 0.014]. During 100–300 ms, we conﬁrmed that there was no
signiﬁcant response in the following contrasts, in which sentence
structure was held constant: Syn, (SV, N, vi) – (SV, A, vt); Syn, (OV, N,vt) – (OV, A, vi); and Syn, (OV, A, vi) – (OV, N, vt). These results further
clariﬁed the speciﬁc temporal dynamics of cortical responses selective
for sentence structure, syntactic anomaly, and verb transitivity, all ofwhich were included in the Syn task.
Discussion
The present study revealed the dynamics of the multiple corticalregions that are involved in the analysis of hierarchical syntactic
structures and task-related information. The Syn-selective responses
to the OV sentences suggest that the left F3t may be critically involved in building sentence structures of a sentence as early as
120 ms from the verb onset (Fig. 3). Moreover, we found selective
responses to the three factors included in the Syn task: sentence
structure, syntactic anomaly, and verb transitivity. Subsequent
responses in the left insula at 150–170 ms were selective for theprocessing of the OV sentence structure (Fig. 4). On the other hand,
responses in the left mediofrontal and inferior parietal regions at
240–280 ms were related to syntactic anomaly and verb transitivity,
respectively (Figs. 5 and 6). Taken together, these results support theaccount of sentence processing proposed in contemporary linguistics,
rather than the linear order model for word sequences.

The activation of the left insula, as well as the adjacent frontal
operculum, has been reported in previous fMRI studies focusing on
syntactic decision (Friederici et al., 2003; Suzuki and Sakai, 2003;
Newman et al., 2003; Tatsuno and Sakai, 2005; Friederici et al., 2006),
and in those focusing on sentence comprehension (Homae et al.,2002). In the present study, the selective responses to the OV sentence
structures in the left insula may reﬂect the processing of more complex
hierarchical structure of the OV sentences (Fig. 1a), which is consistent
with the behavioral results. On the other hand, the left SMG has been
implicated in lexical processing (Corina et al., 2005), the activation ofwhich was enhanced more by vt than vi in a lexical decision task
(Thompson et al., 2007). In the present study, the responses in the left
SMG, showing selectivity to the OV sentences with vt, may reﬂect theprocessing of more detailed lexical information for vt.
Cortical responses to syntactic anomaly and sentence structure
As shown by the behavioral data, the syntactically anomalous
sentences were more demanding than the normal sentences. Previous
studies have suggested that the ACC and OFC are involved in the process
of monitoring and choosing between decision options when theoutcomes of those decisions are uncertain or conﬂicting (Bush et al.,
2000; O'Doherty et al., 2001; Botvinick et al., 2004; Walton et al., 2004).
The effects of syntactic anomaly in the ACC and OFC are consistent withthese reports, in that this monitoring process involves an error
detection, reanalysis, and correction as in our case of syntacticallyanomalous sentences, especially for anomalous SV sentences withinanimate subjects and vt (Table 2). On the other hand, it has beenreported that the event-related potentials (ERPs) at 100–300 ms, known
as early left anterior negativity (ELAN), showed selectivity to thesyntactic anomaly, reﬂecting early phrase structure building processes(Friederici et al., 1993; Hahne and Friederici, 1999). Using MEG, thegenerators of the ELAN were suggested to be localized in the inferior
frontal and anterior temporal cortices (Friederici et al., 2000b), whichwere selected a priori as the seed points. It is possible that the left ACC
and/or OFC, which showed greater responses under the syntactic
anomalous conditions (Fig. 5b), also contribute to the ELAN.

Author's personal copy
K. Iijima et al. / NeuroImage 44 (2009) 1387–1396

Cortical responses to verb transitivity
It has been suggested that a lateral region of the IPL [MNI
coordinates, (−44, −54, 46)] is critical for vocabulary knowledge (Leeet al., 2007), which may be related to the effect of verb transitivity
observed here, i.e., increased responses to the sentences with vt. It isalso possible that the decreased responses to the sentences with vi
reﬂected simpler lexical processing with a single argument of a
subject, consistent with the behavioral data, in which the condition(SV, N, vi) was the least demanding.
Conclusions
Using MEG with the minimal-pair paradigm to compare the Syn and
Sem tasks, we found that the responses to the normal OV sentences in
the left F3t at 120–140 ms were selective for explicit syntactic
processing. The earliest left F3t responses can thus be regarded as
predictive effects for the syntactic information of the next-coming verb,
which cannot be explained by associative memory or statistical factors.
Moreover, the selective responses to the OV sentence structures in theleft insula at 150–170 ms may reﬂect the processing of more complex
hierarchical structure of the OV sentences. The responses in the left
SMG at 190–210 ms, showing selectivity to the OV sentences with vt,
may reﬂect the processing of more detailed lexical information for vt.
On the other hand, the responses in the left ACC and left OFC at 240–
260 ms were related to syntactic anomaly, reﬂecting an error detection,
reanalysis, and correction. The responses in the left IPL at 260–280 ms
were related to verb transitivity, probably reﬂecting lexical processing.
These results revealed the dynamics of the multiple cortical regions thatwork in concert to analyze hierarchical syntactic structures and taskrelated information, further elucidating the top-down syntactic
processing that is crucial during on-line sentence processing.
Acknowledgments
We thank M. Koizumi, T. Yasui, and F. Tani for their helpful
discussion, N. Saeki and N. Komoro for their technical assistance, and
S. Matsukura and H. Matsuda for their administrative assistance. This
work was supported in part by a Core Research of Evolutional Scienceand Technology (CREST) grant from the Japan Science and Technology
Agency (JST) and by a Grant-in-Aid for Scientiﬁc Research on Priority
Areas (“Higher-Order Brain Functions,” 17022013) from the Ministryof Education, Culture, Sports, Science, and Technology of Japan.
References
Ben-Shachar, M., Palti, D., Grodzinsky, Y., 2004. Neural correlates of syntactic
movement: converging evidence from two fMRI experiments. Neuroimage 21,1320–1336.
Bornkessel, I., Zysset, S., Friederici, A.D., von Cramon, D.Y., Schlesewsky, M., 2005. Who
did what to whom? The neural basis of argument hierarchies during language
comprehension. Neuroimage 26, 221–233.
Botvinick, M.M., Cohen, J.D., Carter, C.S., 2004. Conﬂict monitoring and anterior
cingulate cortex: an update. Trends Cognit. Sci. 8, 539–546.
Bush, G., Luu, P., Posner, M.I., 2000. Cognitive and emotional inﬂuences in anterior
cingulate cortex. Trends Cognit. Sci. 4, 215–222.
Chomsky, N., 1981. Lectures on Government and Binding: The Pisa Lectures. Mouton de
Gruyter, Berlin.
Chomsky, N., 1995. The Minimalist Program. The MIT Press, Cambridge, MA.
Cleeremans, A., McClelland, J.L., 1991. Learning the structure of event sequences. J. Exp.
Psychol. 120, 235–253.
Corina, D.P., Gibson, E.K., Martin, R., Poliakov, A., Brinkley, J., Ojemann, G.A., 2005.
Dissociation of action and object naming: evidence from cortical stimulation
mapping. Hum. Brain Mapp. 24, 1–10.
Dale, A.M., Sereno, M.I., 1993. Improved localization of cortical activity by combining
EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cognit.
Neurosci. 5, 162–176.
Dapretto, M., Bookheimer, S.Y., 1999. Form and content: dissociating syntax andsemantics in sentence comprehension. Neuron 24, 427–432.
Elman, J.L., 1991. Distributed representations, simple recurrent networks, and grammatical structure. Mach. Learning 7, 195–225.

1395

Embick, D., Marantz, A., Miyashita, Y., O'Neil, W., Sakai, K.L., 2000. A syntactic
specialization for Broca's area. Proc. Natl. Acad. Sci. U. S. A. 97, 6150–6154.
Friederici, A.D., Pfeifer, E., Hahne, A., 1993. Event-related brain potentials during natural
speech processing: effects of semantic, morphological and syntactic violations.
Cognit. Brain Res. 1, 183–192.
Friederici, A.D., Opitz, B., von Cramon, D.Y., 2000a. Segregating semantic and syntactic
aspects of processing in the human brain: an fMRI investigation of different word
types. Cereb. Cortex 10, 698–705.
Friederici, A.D., Wang, Y.H., Herrmann, C.S., Maess, B., Oertel, U., 2000b. Localization ofearly syntactic processes in frontal and temporal cortical areas: a magnetoencephalographic study. Hum. Brain Mapp. 11, 1–11.
Friederici, A.D., Rüschemeyer, S.-A., Hahne, A., Fiebach, C.J., 2003. The role of left inferior
frontal and superior temporal cortex in sentence comprehension: localizing
syntactic and semantic processes. Cereb. Cortex 13, 170–177.
Friederici, A.D., Fiebach, C.J., Schlesewsky, M., Bornkessel, I.D., von Cramon, D.Y., 2006.
Processing linguistic complexity and grammaticality in the left frontal cortex.
Cereb. Cortex 16, 1709–1717.
Grewe, T., Bornkessel, I., Zysset, S., Wiese, R., von Cramon, D.Y., Schlesewsky, M., 2006.
Linguistic prominence and Broca's area: the inﬂuence of animacy as a linearization
principle. Neuroimage 32, 1395–1402.
Hahne, A., Friederici, A.D., 1999. Electrophysiological evidence for two steps in syntactic
analysis: early automatic and late controlled processes. J. Cognit. Neurosci. 11,
194–205.
Hämäläinen, M., Hari, R., Ilmoniemi, R.J., Knuutila, J., Lounasmaa, O.V., 1993.
Magnetoencephalography — theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Modern Physics 65, 413–497.
Hashimoto, R., Sakai, K.L., 2002. Specialization in the left prefrontal cortex for sentencecomprehension. Neuron 35, 589–597.
Hauser, M.D., Chomsky, N., Fitch, W.T., 2002. The faculty of language: what is it, who has
it, and how did it evolve? Science 298, 1569–1579.
Helenius, P., Salmelin, R., Service, E., Connolly, J.F., 1998. Distinct time courses of word
and context comprehension in the left temporal cortex. Brain 121, 1133–1142.
Homae, F., Hashimoto, R., Nakajima, K., Miyashita, Y., Sakai, K.L., 2002. From perception
to sentence comprehension: the convergence of auditory and visual information of
language in the left inferior frontal cortex. Neuroimage 16, 883–900.
Indefrey, P., Brown, C.M., Hellwig, F., Amunts, K., Herzog, H., Seitz, R.J., Hagoort, P., 2001.
A neural correlate of syntactic encoding during speech production. Proc. Natl. Acad.
Sci. U. S. A. 98, 5933–5936.
Jaeggli, O., 1981. Topics in Romance Syntax. Foris Publications, Dordrecht.
Kang, A.M., Constable, R.T., Gore, J.C., Avrutin, S., 1999. An event-related fMRI study of
implicit phrase-level syntactic and semantic processing. Neuroimage 10, 555–561.
Karniski, W., Blair, C., Snider, A.D., 1994. An exact statistical method for comparing
topographic maps, with any number of subjects and electrodes. Brain Topo. 6,
203–210.
Kinno, R., Kawamura, M., Shioda, S., Sakai, K.L., 2008. Neural correlates of noncanonicalsyntactic processing revealed by a picture-sentence matching task. Hum. Brain
Mapp. 29, 1015–1027.
Kriegeskorte, N., Goebel, R., 2001. An efﬁcient algorithm for topologically correct
segmentation of the cortical sheet in anatomical MR volumes. Neuroimage 14,
329–346.
Lee, H., Devlin, J.T., Shakeshaft, C., Stewart, L.H., Brennan, A., Glensman, J., Pitcher, K.,
Crinion, J., Mechelli, A., Frackowiak, R.S.J., Green, D.W., Price, C.J., 2007.
Anatomical traces of vocabulary acquisition in the adolescent brain. J. Neurosci.
27, 1184–1189.
Newman, S.D., Just, M.A., Keller, T.A., Roth, J., Carpenter, P.A., 2003. Differential effects ofsyntactic and semantic processing on the subregions of Broca's area. Cognit. Brain
Res. 16, 297–307.
Nichols, T.E., Holmes, A.P., 2002. Nonparametric permutation tests for functional
neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1–25.
O'Doherty, J., Kringelbach, M.L., Rolls, E.T., Hornak, J., Andrews, C., 2001. Abstract rewardand punishment representations in the human orbitofrontal cortex. Nat. Neurosci.
4, 95–102.
Oldﬁeld, R.C., 1971. The assessment and analysis of handedness: the Edinburgh
inventory. Neuropsychologia 9, 97–113.
Pantazis, D., Nichols, T.E., Baillet, S., Leahy, R.M., 2005. A comparison of random ﬁeld
theory and permutation methods for the statistical analysis of MEG data.
Neuroimage 25, 383–394.
Pylkkänen, L., Marantz, A., 2003. Tracking the time course of word recognition with
MEG. Trends Cognit. Sci. 7, 187–189.
Röder, B., Stock, O., Neville, H., Bien, S., Rösler, F., 2002. Brain activation modulated by
the comprehension of normal and pseudo-word sentences of different processing
demands: a functional magnetic resonance imaging study. Neuroimage 15,1003–1014.
Saito, M., Fukui, N., 1998. Order in phrase structure and movement. Ling. Inq. 29,
439–474.
Sakai, K.L., 2005. Language acquisition and brain development. Science 310, 815–819.
Sakai, K.L., Noguchi, Y., Takeuchi, T., Watanabe, E., 2002. Selective priming of syntactic
processing by event-related transcranial magnetic stimulation of Broca's area.
Neuron 35, 1177–1182.
Shibatani, M., 1990. The Languages of Japan. Cambridge University Press, Cambridge,
UK.
Stromswold, K., Caplan, D., Alpert, N., Rauch, S., 1996. Localization of syntactic
comprehension by positron emission tomography. Brain Lang. 52, 452–473.
Suzuki, K., Sakai, K.L., 2003. An event-related fMRI study of explicit syntactic processingof normal/anomalous sentences in contrast to implicit syntactic processing. Cereb.
Cortex 13, 517–526.

Author's personal copy
1396

K. Iijima et al. / NeuroImage 44 (2009) 1387–1396

Talairach, J., Tournoux, P., 1988. Co-Planar Stereotaxic Atlas of the Human Brain. 3Dimensional Proportional System: An Approach to Cerebral Imaging. Thieme,
Stuttgart.
Tatsuno, Y., Sakai, K.L., 2005. Language-related activations in the left prefrontal regions
are differentially modulated by age, proﬁciency, and task demands. J. Neurosci. 25,
1637–1644.

Thompson, C.K., Bonakdarpour, B., Fix, S.C., Blumenfeld, H.K., Parrish, T.B., Gitelman,
D.R., Mesulam, M.M., 2007. Neural correlates of verb argument structure
processing. J. Cognit. Neurosci. 19, 1753–1768.
Walton, M.E., Devlin, J.T., Rushworth, M.F.S., 2004. Interactions between decision
making and performance monitoring within prefrontal cortex. Nat. Neurosci. 7,
1259–1265.