doi:10.1093/brain/awu013

Brain 2014: 137; 1193–1212| 1193BRAIN
A JOURNAL OF NEUROLOGY

Differential reorganization of three syntax-related
networks induced by a left frontal glioma
Ryuta Kinno,1,2,3 Shinri Ohta,4,5 Yoshihiro Muragaki,3,6 Takashi Maruyama3,6 andKuniyoshi L. Sakai1,3,4

Correspondence to: Kuniyoshi L. Sakai
Department of Basic Science,
Graduate School of Arts and Sciences,
The University of Tokyo,
3-8-1 Komaba, Meguro-ku,
Tokyo 153-8902, Japan
E-mail: sakai@mind.c.u-tokyo.ac.jp

The opercular/triangular parts of the left inferior frontal gyrus and the left lateral premotor cortex are critical in syntactic
processing. We have recently indicated that a glioma in one of these regions is sufficient to cause agrammatic comprehension.
In the present study, we aimed to show how normally existing syntax-related networks are functionally reorganized by a lesion.
Twenty-one patients with a left frontal glioma preoperatively performed a picture–sentence matching task, and underwent
functional magnetic resonance imaging scans in an event-related design. We established two qualitatively different types ofagrammatic comprehension, depending on glioma location. Patients with a glioma in the left lateral premotor cortex had a more
profound deficit in the comprehension of scrambled sentences than that of active and passive sentences. In contrast, patientswith a glioma in the opercular/triangular parts of the left inferior frontal gyrus had a more profound deficit in the comprehensionof passive and scrambled sentences than that of active sentences. Moreover, we found dramatic changes in the activation
patterns in these two patient groups, which accompanied abnormal overactivity and/or underactivity in the syntax-related
regions. Furthermore, by examining functional connectivity in the normal brain, we identified three syntax-related networks
among those regions, and anatomically visualized connections within individual networks by using diffusion tensor imaging.
The first network consists of the opercular/triangular parts of the left inferior frontal gyrus, left intraparietal sulcus, right frontal
regions, presupplementary motor area, and right temporal regions. These regions were overactivated in the patients with aglioma in the left lateral premotor cortex only for correct responses, indicating a cognitive change. The second network consistsof the left lateral premotor cortex, left angular gyrus, lingual gyrus, and cerebellar nuclei. These regions were overactivated in
the patients with a glioma in the opercular/triangular parts of the left inferior frontal gyrus for both correct and incorrect
responses, indicating a neuronal change. The third network consists of the left ventral frontal and posterior temporal regions.
These regions were underactivated in the patients with a glioma in the opercular/triangular parts of the left inferior frontal
gyrus, indicating another neuronal change. These results demonstrate that agrammatic comprehension is associated with theglobal reorganization of functionally distinct networks, which indeed reflects a differential change in the relative contribution ofthese three networks to normal syntax-related functions.

Received July 11, 2013. Revised November 19, 2013. Accepted December 9, 2013. Advance Access publication February 11, 2014
ß The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
For Permissions, please email: journals.permissions@oup.com

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1 Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
2 Division of Neurology, Department of Internal Medicine, Showa University Northern Yokohama Hospital, Kanagawa 224-8503, Japan
3 CREST, Japan Science and Technology Agency, Tokyo 102-0075, Japan
4 Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
5 Japan Society for the Promotion of Science, Tokyo 102-8472, Japan
6 Department of Neurosurgery, Tokyo Women’s Medical University, Tokyo 162-8666, Japan

1194| Brain 2014: 137; 1193–1212

R. Kinno et al.

Keywords: agrammatism; language; glioma; frontal cortex; MRI
Abbreviations: F3 = inferior frontal gyrus; LPMC = lateral premotor cortex

Introduction

Materials and methods
Here, we provided an overview of the experimental conditions andprocedures used to acquire the data presented; full details are provided
in the Supplementary material.

Participants
We tested 21 patients, who were native Japanese speakers newly
diagnosed as having a left frontal glioma (Table 1). The patients preoperatively performed a picture–sentence matching task (Fig. 1A), andunderwent functional MRI scans in an event-related design. The patients were divided into three groups based on the individual tumour
locations in the normalized brain (Fig. 1B): patients with a glioma in
the left LPMC (LPMC group, n = 7); patients with a glioma in the
opercular/triangular parts of the left F3 (F3 group, n = 7); and patientswith a glioma in the other left frontal regions (Other group, n = 7).
The categorization criterion of each group was whether or not theglioma of a patient overlapped, at least partially on a voxel-by-voxel
basis, with functionally identified regions in our previous study (Kinno
et al., 2008): the left LPMC and opercular/triangular parts of the left
F3 shown in Figs 3 and 4 in Kinno et al. (2008), respectively.

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Cortical reorganization is one of the fundamental issues in clinicalneuroscience. For example, homotopic reorganization, i.e. activation in a contralesional homologous region, is induced by acutelesions after stroke (Saur et al., 2006) or by slow-growing lesions
such as gliomas (Desmurget et al., 2007), as observed by functional MRI. However, homotopic reorganization occurs only in a
subset of patients, and its exact neural mechanisms remain unknown. Although neurovascular uncoupling (Ulmer et al., 2004)
or an abnormality of transcallosal inhibition leading to disinhibition(Thiel et al., 2006) has been considered to explain such reorganization, these theories are still not conclusive. Therefore, differential activation patterns between patients and normal participants
should be thoroughly examined by using functional and anatomical imaging techniques. Abnormal activity in patients is indicatedby overactivity (or underactivity), in which a given region is recruited more (or less) than normal during a cognitive task, andsuch an abnormality reflects cognitive and/or neuronal changes.
Although neuronal changes are mediated by changes in thestrength of pre-existing connections, cognitive changes occur
when a patient uses a different set of cognitive processes, either
because of increased demands on normal processes, or because a
new cognitive procedure has been learned (Price and Friston,1999). In the present study with agrammatic patients, we focusedon the fundamental syntactic processes. We aimed to show how
normally existing syntax-related networks are functionally reorganized by a lesion.
Previous functional neuroimaging studies of normal participants
have already established that the opercular/triangular parts of theleft inferior frontal gyrus (F3), as well as the left lateral premotor
cortex (LPMC), play a crucial role in syntactic processes(Stromswold et al., 1996; Dapretto and Bookheimer, 1999;
Embick et al., 2000; Hashimoto and Sakai, 2002; Sakai et al.,
2002; Friederici et al., 2003; Musso et al., 2003; Suzuki andSakai, 2003); these regions have been proposed as putative grammar centres (Sakai, 2005). Moreover, we clarified that these tworegions are differentially modulated during syntactic processing(Kinno et al., 2008). For patients with a left frontal glioma, wehave recently indicated that a glioma in one of these regions issufficient to cause agrammatic comprehension (Kinno et al.,2009). In the present functional MRI study, we analysed activation
patterns of agrammatic patients with a left frontal glioma to identify any cortical reorganization of syntax-related networks.
We predict that there are multiple syntax-related networks,
each of which partially includes lateral sides of frontal, temporal,
and parietal regions, as well as medial regions and cerebellum.
These candidate regions have been already indicated by our previous functional MRI studies (Suzuki and Sakai, 2003; Kinno et al.,2008). We further hypothesize that there exist at least three
syntax-related networks, corresponding to three regions of theleft LPMC, opercular/triangular parts of the left F3, andtriangular/orbital parts of the left F3, which were identified tohave distinct functional roles during sentence comprehension(Sakai, 2005). By using functional MRI data in the present
study, functional connectivity among activated regions was assessed to reveal these three sets of functionally correlated regions
during a syntactic task. We used all time-series data from thenormal participants alone, as abnormal brains might reflect coincidental co-activation or co-deactivation.
The recent diffusion tensor imaging studies have suggested two
different pathways for language processing in the normal or abnormal brain: the dorsal tracts of the arcuate and superior longitudinal fasciculi, and ventral tracts of the middle longitudinalfasciculus and extreme capsule (Saur et al., 2008; Rolheiser
et al., 2011; Wilson et al., 2011; Wong et al., 2011). The
dorsal pathways may be further divided into two: one connecting
the LPMC and temporal gyrus for sensory to motor mapping, theother connecting the F3 and temporal gyrus for semantic andsyntactic functions in sentence comprehension (Friederici, 2011).
However, the relative contributions of these pathways to syntactic
processing and related functions, as well as their functional divisions within the syntax-related networks, remain unclear. As asubstantial amount of white matter is usually included in eachpatient’s tumour (see the grey matter ratio in Table 1), any
fibres originated from the focal lesion may also have been partially
defective. Therefore, to provide empirical backup for the networks
deduced from the activation studies of the normal participants, wereferred to normal data of fibre tracking. Our current results will
clarify the principles of global reorganization within the syntaxrelated networks induced by focal lesions.

31
47
36
49
34
29
27
36  8.6

32
20
31
32
42
29
47
33  8.8

62
24
38
21
29
25
36
34  14

M
F
M
M
F
F
M

F
M
F
F
F
M
M

Age

M
M
M
M
F
M
F

Gender

89
68
100
100
100
100
100
83  12

54
100
100
100
90
87
73
86  17

88
81
88
100
100
81
45
83  19

Laterality
quotient

98/101
94/98
106/113106/106
86/88
104/101
90/99
98  8.0/101  7.6

101/9898/101
99/10095/10995/10991/93
93/10096  3.5/101  5.8

102/92
105/102
102/92
98/96
99/90
106/102
101/95102  3.0/96  4.8

Verbal/non-verbal IQ

Left
Left
Left
Left
Left
Left
Left

Left
Left
Left
Left
Left
Left
Left

Left
Left
Left
Left
Left
Left
Left

Hemispheric
dominance

L. F1/F2/SMA
L. F1/F2/SMA/striatum
L. F1/F2/SMA/striatum
L. F1/F2/F3t/striatum
L. F1/F2/F3O/striatum
L. F1/F2/F3t
L. F2/F3O
43 283  21 323

L. F1/F2/F3op/F3O/insula
L. F1/F2/F3op/F3O/insula
L. F1/F2/F3op/F3t/insula/striatum
L. F1/F2/F3op/F3O/insula/striatum
L. F2/F3op/F3O/insula/striatum
L. F2/F3op/F3O/insula/striatum
L. F3op/F3t/insula/striatum
48 643  22 555

L. F1/F2/SMA/LPMC
L. F1/F2/SMA/LPMC/F3op
L. F1/F2/SMA/LPMC/F3op
L. F1/F2/LPMC
L. F1/F2/LPMC/F3op
L. F1/F2/LPMC/F3op
L. F2/ LPMC/F3op
40 338  22 300

Tumourlocation
Grey matter
ratio
57.3
56.3
59.8
58.0
52.6
50.5
56.9
56  3.2
59.0
52.2
58.1
56.3
59.8
50.9
54.3
56  3.4
55.3
58.0
54.7
52.6
50.1
53.7
56.7
54  2.6

Tumourvolume (mm3)40 09633 21489 13730 14829 37538 00322 39325 47174 55653 29757 65074 54336 86318 12049 30163 79551 08163 05148 93214 66512 158

AO
OA
AOA
DA
AO
DA
OA

AOA
AO
AOA
DA
OA
OD
DA

AO
AOA
AA
AO
OD
AO
DA

Tumour
type

III
II
III
II
III
II
II

III
III
III
II
II
II
II

III
III
III
III
II
III
II

Tumourgrade

Brain 2014: 137; 1193–1212

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014aSeven patients were from our previous study (Kinno et al., 2009). The laterality quotient of handedness was determined by the Edinburgh handedness inventory (Oldfield, 1971). Magnetic resonance images were normalized with
SPM8 to determine the tumour location and volume (mm3), as well as the grey matter ratio (%) of a tumour including the grey matter and white matter. The determination of tumour types and grades (II or III, with III being more
severe) was based on the World Health Organization Classification of Tumours of the Nervous System (2000).
AA = anaplastic astrocytoma (grade III); AO = anaplastic oligodendroglioma (grade III); AOA = anaplastic oligoastrocytoma (grade III); DA = diffuse astrocytoma (grade II); F = female; F1 = superior frontal gyrus; F2 = middle frontal
gyrus; F3O = orbital part of the F3; F3op = opercular part of the F3; F3t = triangular part of the F3; IQ = intelligence quotient; L = left; M = male; OA = oligoastrocytoma (grade II); OD = oligodendroglioma (grade II); SD = standard
deviation; SMA = supplementary motor area.

LPMC group
Patient 1
Patient 2a
Patient 3a
Patient 4
Patient 5
Patient 6
Patient 7a
Mean  SD
F3 group
Patient 8
Patient 9
Patient 10
Patient 11
Patient 12a
Patient 13
Patient 14a
Mean  SD
Other group
Patient 15a
Patient 16
Patient 17
Patient 18
Patient 19a
Patient 20
Patient 21
Mean  SD

Patient

Table 1 Patient demographics

Reorganization by a left frontal glioma| 1195

1196| Brain 2014: 137; 1193–1212

R. Kinno et al.

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Figure 1 Syntactic task, glioma locations, and agrammatic comprehension. (A) A picture–sentence matching task under either twoargument conditions or one-argument condition; each stimulus consisted of one picture (top) and one sentence (bottom). Picturesconsisted of two stick figures, each of which was distinguished by a ‘head’ symbol: a circle, square or triangle. We used four kinds ofgrammatical particles, which represent the syntactic information in Japanese: -ga, a nominative case marker; -ni, a dative case marker; -o,an accusative case marker; and -to, a coordinator (and). Under the two-argument conditions (marked in red), we tested three sentence
types: active sentences (e.g. ‘-ga *-o hiiteru’ and ‘*-ga -o hiiteru’), passive sentences (e.g. ‘*-ga -ni hikareru’ and ‘-ga *-ni
hikareru’), and scrambled sentences (e.g. ‘*-o -ga hiiteru’). Examples of matched and mismatched sentences are shown in the first andsecond rows, respectively. Under the one-argument condition (marked in green), we presented simpler sentences (e.g. ‘&-to -ga
aruiteru’ and ‘&-to -ga koronderu’). Examples of matched and mismatched sentences are shown in the left and middle panels of thethird row, respectively. For a control task (marked in blue), a matched example is shown in the right panel of the third row. (B) Lesion
overlap maps for patient groups. For each group, the full extent of gliomas was overlaid and circled with coloured lines: LPMC group (red),(continued)

Reorganization by a left frontal glioma

Lesion analyses
The glioma was first identified on the normalized T1-weighted structural image, and the glioma boundary was semi-automatically determined using the 3D Fill tool in MRIcroN software, which generated acontiguous cluster of voxels defined by the intensity of the glioma
itself. The boundary of each lesion, including brain oedemas andabnormalities of perfusion, was confirmed with T2-weighted magnetic
resonance images taken at the Department of Neurosurgery, Tokyo
Women’s Medical University. The absence of any skip lesions distantfrom a tumour was confirmed with 11C-methionine, 18F-fluorodeoxyglucose, and 11C-choline PET data (resolution = 4.8  4.8  4.25 mm3)
taken at the Chubu Medical Centre for Prolonged Traumatic Brain
Dysfunction (Minokamo City, Gifu, Japan).

Stimuli
Each visual stimulus consisted of a picture with head symbols (circle,
square or triangle) at the top, and of an always grammatical sentenceat the bottom (Fig. 1A). For each stimulus, we chose two different
head symbols. The sentences describing actions were written using a
combination of the hiragana and kanji writing systems. Using the sametask, we tested two types of conditions with different sets of stimuli:
two-argument and one-argument conditions. Under the two-argument conditions with an identical picture set, we tested three different
sentence types: active, passive, and scrambled sentences. Scrambled
sentences are perfectly normal.

| 1197

Under the two-argument conditions, each sentence ended with atransitive verb, and had two arguments (phrases associated with thepredicate) with different grammatical relations (subject, direct object orindirect object) and semantic roles (agent, experiencer or patient).
More specifically, the active, passive, and scrambled sentences corresponded to ‘subject and direct object’ (agent and patient), ‘subject andindirect object’ (experiencer and agent), and ‘direct object and subject’(patient and agent) types, respectively.
Under the one-argument condition, each sentence ended with an
intransitive verb, and corresponded to a ‘double subjects’ (double
agents) type, which did not involve two-argument relationships. A
linguistically meaningful contrast is thus ‘Two-argument Oneargument’, where we averaged together activations under theactive, passive, and scrambled sentence conditions. This contrast
mainly involved syntactic processes, together with minimal semantic
processes of semantic role assignment (experiencer/patient) andlexico-semantics (verb types), whereas general cognitive processes
were well controlled. All stimuli were presented visually in yellow
against a dark background (Fig. 1A). Each stimulus was presented
for 5800 ms (intratrial interval) followed by a 200 ms blank interval.

Task
In the picture–sentence matching task, the participants read a sentence
covertly and judged whether or not the action depicted in a picture
matched the meaning of the sentence. They responded by pressing
one of two buttons in a row. Using the same stimulus sets of picturesand letters presented under both two-argument and one-argument
conditions, we tested a control task, in which the participants judged
whether or not two head symbols in the picture matched those at thebottom, irrespective of their order (Fig. 1A). The letters in hiragana
were jumbled without changing the head symbols and kanji, so thatthe letter string prevented even basic word recognition. General cognitive factors such as visual perception of the stimuli, matching, response selection, and motor responses were controlled by the control
task, and by the one-argument condition.
A single run of the task sessions (306 s) contained 24 ‘test events’ of
the picture–sentence matching task (six times each under the active,
passive, and scrambled sentence conditions, as well as under the oneargument condition), with variable intertrial intervals of one (6 s) or
two (12 s) control tasks. The order of the test events was pseudorandomized without repetition of the same condition to prevent any condition-specific strategy. Eight runs were tested per participant in a day.

Magnetic resonance imaging
Data acquisition
The functional MRI scans were conducted on a 1.5 T scanner (Stratis
II, Premium; Hitachi Medical Corporation), using a gradient-echo
echo-planar imaging sequence (repetition time = 3 s, echo

Figure 1 Continued
F3 group (green), and Other group (yellow). A left lateral surface (horizontal cross hairs at z = 42 and 12; vertical cross hairs at y = 33 and
9) of a standard brain is shown, together with its coronal and axial slices at cross hairs. The colour scale denotes the number of patients(n 5 3). (C–F) Histograms for the error rates of the LPMC group (C), F3 group (D), Other group (E), and normal control subjects (F). Notethat the performances for both the LPMC and F3 groups were significantly impaired under the two-argument conditions, but not under
the one-argument condition. Error bars indicate the standard error of the mean for the participants, and asterisks denote the significant
differences among the two-argument conditions (corrected P 5 0.05). Acc = accusative case; Dat = dative case; L = left;
Nom = nominative case.

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The patients reported here underwent surgery at the Department of
Neurosurgery, Tokyo Women’s Medical University, after behaviouraland functional MRI assessment at the University of Tokyo, Komaba.
The following conditions comprised the criteria for inclusion of 21
patients in the present study (Table 1): (i) right-handedness; (ii) no
deficits in verbal/written communication or other cognitive abilities
reported by the patients or physicians; (iii) no history of neurologicalor psychiatric disorders other than glioma and seizures; (iv) freedom
from seizures with or without antiepileptic drugs; (v) no medical problems related to MRI acquisition; and (vi) completion of at least threefunctional MRI runs without significant head movement. The laterality
quotient of handedness was determined by the Edinburgh handedness
inventory (Oldfield, 1971).
In the present study, we recruited seven normal age-matched participants for functional MRI experiments [normal group; five males andtwo females, aged 25–43 years, 31  5.9 (mean  standard deviation)]. Eleven normal age-matched participants (10 males and one
female, aged 19–40 years, 29  6.0) were also tested in the diffusion
tensor imaging studies. Written informed consent was obtained fromeach participant after the nature and possible consequences of thestudies were explained. Approval for the experiments was obtained
from the institutional review board of the University of Tokyo,
Komaba, as well as of the Tokyo Women’s Medical University.

Brain 2014: 137; 1193–1212

1198| Brain 2014: 137; 1193–1212

time = 50.5 ms, flip angle = 90 , field of view = 192  192 mm2, resolution = 3  3 mm2). The diffusion tensor imaging scans were conducted on a 3.0 T scanner equipped with an 8-channel phased-arrayhead coil (Signa HDxt; GE Healthcare), using a diffusion-weighted
spin-echo echo-planar imaging sequence (b-value = 1000 s/mm2,
repetition
time = 15 s,
echotime = 86.6 ms,
fieldofview = 256  256 mm2, resolution = 2  2 mm2, number of excitations = 2). A single image without diffusion-weighting (b0) was initiallyacquired, and then diffusion-weighting was isotropically distributed
along 60 diffusion-encoding gradient directions.

R. Kinno et al.
(Behin et al., 2003). There were two patients with astrocytic tumours in each patient group. As regards tumour grades, grade II
gliomas grow slowly at a constant and continuous rate of 4 mm(in diameter) per year (Mandonnet et al., 2003). Moreover, grade
II gliomas systematically change into grade III gliomas (i.e. they
undergo anaplastic transformation) within a median of 7–8 years(Duffau, 2005). In the present study, the LPMC group included
fewer patients with grade II gliomas, as well as fewer females,than the F3 and Other groups; these two factors are re-examined
after the behavioural and activation data are presented.

Data analyses

Results
Demographics of the patient groups
We characterized the three patient groups to examine whether
there were any quantitative or qualitative differences among
them (Table 1). The patients’ verbal and non-verbal intelligencequotients (both ranges, 86–113 for all patients) were within one
standard deviation (  15) of the mean. According to ANOVA witha factor of group (LPMC, F3, Other), there were no significant
differences among the groups in age, laterality quotient of handedness, verbal/non-verbal intelligence quotients, tumour volume
or grey matter ratio (P 4 0.1). Moreover, the error rates undereach two-argument condition could not be attributed to thesefactors, according to the correlation analyses among all patients(P 4 0.1). Each patient group included two patients without antiepileptic drug treatment. There are three types of grade II and III
gliomas: astrocytic tumours (i.e. diffuse and anaplastic astrocytoma), oligodendroglial tumours (i.e. oligodendroglioma andanaplastic
oligodendroglioma),andmixed-type
gliomasAgrammatic comprehension caused by aleft frontal glioma
According to previous lesion studies (Schwartz et al., 1980; Caplan
et al., 1985; Goodglass and Menn, 1985; Menn and Obler, 1990;
Pulvermüller, 1995; Grodzinsky, 2000; Kinno et al., 2009), agrammatic patients show relatively good comprehension of single
words and simple sentences, but have trouble understanding sentences with more complex syntactic structures. In the present
study, we set a two-level criterion as follows. The first-level criterion is an impaired comprehension under the two-argument conditions when compared with the normal participants, in spite of anormal comprehension under the simpler one-argument condition.
This criterion precludes any general disorders as a result of visual/motor impairments, attentional disturbances (drowsiness or dizziness) or perseveration for particular responses. In both functional
MRI and lesion studies, we have previously indicated that thepassive and scrambled sentences required more syntactic analyses
for the two-argument relationships than the active sentences thatwere canonical (Kinno et al., 2008, 2009). The second-level criterion is the higher error rates under the passive and/or scrambled
than the active sentence conditions, clearly indicating a syntactic
problem, i.e. agrammatic comprehension.
To improve the statistical power of behavioural data, we added
21 normal participants, taken from our previous study with thesame experimental protocol (Kinno et al., 2009), to the normalgroup (normal control subjects; total n = 28). The behaviouraldata of error rates and reaction times are shown in Table 2.
Figure 1C–F shows the error rates for each group, which exhibited
marked differences among the patient groups. Under each of the
two-argument conditions, an ANOVA with a factor of group(LPMC, F3, Other, normal control subjects) showed that there
was a clear difference among the groups [active: F(3,48) = 9.7,
P 5 0.0001; passive: F(3,48) = 33, P 5 0.0001; scrambled:
F(3,48) = 41, P 5 0.0001]. Under all of these two-argument conditions, a Dunnett test showed significantly higher error rates inboth the LPMC and F3 groups than the normal control subjects(corrected P 5 0.05), whereas there was no significant difference
between the Other group and normal control subjects (corrected
P 4 0.6). Under the one-argument condition, in contrast, there
was no significant difference among the groups [F(3,48) = 0.40,
P = 0.76], confirming that the basic comprehension of sentences
required by the one-argument condition was preserved among allof the patients. These results clearly indicate that both the LPMC
and F3 groups had trouble understanding sentences with the

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Both group and single-subject analyses were performed in a standard
manner using SPM8 statistical parametric mapping software(Wellcome Trust Centre for Neuroimaging). To discount any generaleffects associated with performance differences among the participants, individual error rates averaged among the two-argument
conditions, or those of the one-argument condition for the
One-argument Control contrast, were entered as a nuisance factor
in a second-level analysis. The results of paired t-tests for the
Two-argument Control, One-argument Control, and Twoargument One-argument contrasts were thresholded at P 5 0.005
for the voxel level, and at corrected P 5 0.05 for the cluster level,with topological false discovery rate correction across the wholebrain, whereas the results of an analysis of covariance with F-test
were thresholded at P 5 0.005 for the voxel level, and at corrected
P 5 0.05 for the cluster level, with family-wise error correction acrossthe whole brain.
By using functional MRI data, functional connectivity among multiple regions was assessed by a partial correlation method for the timeseries data of the normal group. From each of the time-series of two
regions in question, we regressed out all the other nodes, before
estimating the correlation between the two. Data analyses of diffusion
tensor imaging were performed using FSL [Oxford Centre for
Functional MRI of the Brain’s (FMRIB) Software Library 4.1.7] andFDT (FMRIB’s Diffusion Toolbox 2.0). To find the connections between two regions of interest, we set two seed masks and retained
only those tracts that passed through both seed masks.

Reorganization by a left frontal glioma

Brain 2014: 137; 1193–1212| 1199

Table 2 Behavioural data under each condition
Error rates (%)
Participant

Activesentence

Passivesentence

Scrambled
sentence

Oneargument

Control

Activesentence

Passivesentence

Scrambled
sentence

Oneargument

Control

2.2  2.6

1.8  2.0

2.2  1.2

3052  684

3129  642

3242  576

2644  555

2766  426

27.1
66.7
54.2
16.7
31.3
66.7
20.8
40.5  21.5

0.0
6.3
6.3
0.0
2.1
2.1
2.1
2.7  2.6

1.9
0.9
2.8
4.6
2.3
2.8
2.8
2.6  1.1

3385
3898
3470
3105
2821
3255
4184
3445  465

3218
4004
3479
3259
2880
3391
4286
3502  484

3397
3948
3770
2908
2793
3180
4249
3464  546

3212
2945
2958
2667
2486
2607
2739
2802  249

2869
2923
3178
2676
2312
3021
2662
2806  284

27.1
39.6
31.3
16.7
16.7
31.3
8.3
24.4  10.9

2.1
0.0
0.0
4.2
4.2
2.1
4.2
2.4  1.9

0.5
3.2
2.8
1.4
2.3
3.7
2.3
2.3  1.2

3673
2908
3211
3350
3325
3512
3783
3395  294

3720
3363
3594
3242
2777
3327
3850
3410  356

3742
2995
3301
3103
3387
3757
3915
3457  353

3214
2772
2846
2712
2925
1831
2895
2742  433

2406
3190
2262
2414
3095
2764
2867
2714  362

6.3
2.1
0.0
6.3
0.0
0.0
0.0
2.1  3.0

6.3
4.2
0.0
4.2
0.0
0.0
0.0
2.1  2.7

0
2.3
1.9
2.3
4.6
1.4
1.9
2.1  1.4

3867
3322
2306
3148
3067
3185
3237
3161  460

3949
2925
2816
3106
3949
2621
3632
3285  551

4385
3524
2854
3165
3985
3453
3674
3577  507

2959
2509
2754
2407
2959
2737
2818
2735  211

2833
2890
2403
3179
2463
2600
2871
2748  274

Error rates represent per cent incorrect responses per individual per condition; the accuracy is 100
SD = standard deviation.

two-argument relationships, thus satisfying the first-level criterion.
Moreover, a direct comparison of these two groups with a t-testclarified that the F3 group showed significantly higher error rates
than the LPMC group under the passive sentence condition[t(12) = 3.3, P = 0.0064]. This result demonstrates that the deficitsof the LPMC group were qualitatively different from those of theF3 group. As regards reaction times, there was no significant difference among the groups under each of the two-argument conditions, as well as under the one-argument condition (ANOVA,
P 4 0.3).
For the second-level criterion, separate comparisons of error
rates among the two-argument conditions were performed in individual groups. According to a repeated measures ANOVA with afactor of condition (active, passive, scrambled), there was a significant difference in error rates among the conditions in the
LPMC group [F(2,12) = 16, P = 0.0004] and F3 group[F(2,12) = 14, P = 0.0006], but not in the Other group[F(2,12) = 0.16, P = 0.86] or in the normal control subjects[F(2,54) = 1.6, P = 0.21]. In the LPMC group, the error rates
were significantly higher under the scrambled than the active
and passive sentence conditions (Bonferroni/Dunn test, corrected
P 5 0.05) (Fig. 1C). In the F3 group, the error rates were

error rates (%). Reaction times were obtained for correct trials only.

significantly higher under the passive and scrambled than theactive sentence conditions (corrected P 5 0.05) (Fig. 1D). These
results satisfy the second-level criterion, and thus the patients in
the LPMC and F3 groups had agrammatic comprehension. The
normal error rates in the Other group (Fig. 1E) indicate that general medical conditions, including the effect of antiepileptic drug,
did not affect patients’ performances. As regards reaction times, incontrast, there was a significant difference among the two-argument conditions in the Other group [F(2,12) = 4.2, P = 0.042] andnormal control subjects [F(2,54) = 4.6, P = 0.014], but neither inthe LPMC group [F(2,12) = 0.43, P = 0.66] nor in the F3 group[F(2,12) = 0.16, P = 0.85]. In both the Other group and normal
control subjects, the reaction times were significantly longer under
the scrambled than the active sentence condition (corrected
P 5 0.05), normally distinguishing these two conditions.

Activation of the syntax-related regions
in normal participants
For the functional MRI data, each subtracted side of a contrast
between two conditions (e.g. two-argument out of the

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

Normal control subjects (n = 28)
Mean  SD
3.1  4.1
2.9  3.1
LPMC group
Patient 1
16.7
6.3
Patient 2
12.5
12.5
Patient 3
16.7
6.3
Patient 4
8.3
6.3
Patient 5
14.6
16.7
Patient 6
12.5
12.5
Patient 7
0.0
0.0
Mean  SD 11.6  5.9
8.7  5.6
F3 group
Patient 8
12.5
27.1
Patient 9
8.3
27.1
Patient 10
12.5
39.6
Patient 11
8.3
16.7
Patient 12
8.3
16.7
Patient 13
27.1
39.6
Patient 14
0.0
8.3
Mean  SD 11.0  8.2 25.0  11.9
Other group
Patient 15
8.3
6.3
Patient 16
2.1
4.2
Patient 17
0.0
0.0
Patient 18
2.1
2.1
Patient 19
0.0
0.0
Patient 20
0.0
0.0
Patient 21
0.0
0.0
Mean  SD
1.8  3.0
1.8  2.6

Reaction times (ms)1200

| Brain 2014: 137; 1193–1212

R. Kinno et al.

argument Control (A), One-argument Control (B), Two-argument One-argument (C), and Picture-sentence matching Control(D) contrasts. The results of paired t-test were thresholded at P 5 0.005 for the voxel level, and at corrected P 5 0.05 for the cluster level,with family-wise error correction across the whole brain using SPM2. See Supplementary Table 1 for the stereotactic coordinates of theactivation foci (A–C). Each yellow dot represents the local maximum of an activated region shown in Fig. 7, all of which were included in
the regions identified by the Picture-sentence matching Control contrast shown here. L = left.

‘Two-argument One-argument’ contrast) was compared between trials with correct and incorrect responses beforehand in a
first-level analysis of each participant [e.g. Two-argument(correct – incorrect) – One-argument (all trials)]. This procedure
removed any effects of abnormal medical conditions (e.g. anxietytension state), that were included in both correct and incorrect
responses, as much as possible from cortical activations. For aseparate examination of trials with correct and incorrect responses[e.g. Two-argument (correct or incorrect) – One-argument (alltrials)], the use of an identical reference was mandatory for an
exact comparison of both trial types (Fig. 7B), preferably including
both correct and incorrect trials in the subtracting side. Therefore,
we used the same reference of one-argument (all trials) for acomparison with Two-argument (correct – incorrect) as well (seeFig. 7A). We also confirmed that the Two-argument (correct –incorrect) – One-argument (correct – incorrect) contrast resulted
in the same activation patterns presented here.

Here we reanalysed the previous functional MRI data takenfrom 14 normal participants (Kinno et al., 2008), in which we
had used different MRI acquisition parameters and a shorter intratrial interval (3800 ms instead of 5800 ms). We first tested the
Two-argument Control contrast (Fig. 2A and Supplementary
Table 1). This contrast revealed significant activation in the bilateral regions of the LPMC, opercular/triangular/orbital parts of theF3, posterior superior/middle/inferior temporal gyri, angular gyrus,
and intraparietal sulcus, as well as in the presupplementary motor
area and precuneus. In the One-argument Control contrast, significant activation was limited to the bilateral regions of the LPMC
and posterior superior/middle temporal gyri, as well as the left
intraparietal sulcus (Fig. 2B), whereas in the Two-argument
One-argument contrast, activation was clearly localized in theleft frontal and temporal regions alone (Fig. 2C). To visualize an
overall activation pattern in the normal participants, we also tested
the Picture–sentence matching Control contrast (Fig. 2D; see

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

Figure 2 Reanalyses of previous data for normal participants (n = 14) (Kinno et al., 2008). Significant regions were identified by the Two-

Reorganization by a left frontal glioma

Brain 2014: 137; 1193–1212| 1201

region [F(3,18) = 3.2, P = 0.050], was observed with no interaction [F(6,36) = 1.9, P = 0.12]. According to a Bonferroni/Dunn
test, the activation modulation among the two-argument conditions in the Other group exactly replicated that in the normalgroup (corrected P 5 0.05). In both the LPMC and F3 groups,
in contrast, the activation modulation in these four regions wascompletely lost (Fig. 3C–F), with no significant main effect of condition (P 4 0.3). It is striking to note that, in the F3 group, activation was absent in all of these regions. These results suggestthat the loss of activation modulation in these regions is a good
indicator of agrammatic comprehension.

Loss of activation modulation in thesyntax-related regions of agrammaticpatients

Syntax-related activation patterns in thenormal group

We assessed differences in activation among the patient groups by
performing an analysis of covariance with two factors [Group(LPMC, F3, Other)  Condition (active, passive, scrambled)]. A
significant main effect of group was observed in the opercular/
triangular/orbital parts of the left F3 (corrected P 5 0.05) (Fig. 3A
and Supplementary Table 2), probably reflecting the underactivity
and/or overactivity of these regions as a result of lesions in somepatient groups. We also found a significant interaction of group by
condition in the left LPMC, in the opercular/triangular/orbital
parts of the left F3, and in the left posterior superior/middle temporal gyri (Fig. 3B). These regions were all left-lateralized and actually recruited in normal sentence processing (Fig. 2C). On theother hand, there was no significant main effect of condition in
any regions; any such effect was likely masked by the conditionindependent responses in some patient groups.
To further clarify the activation modulation among the twoargument conditions, the per cent signal changes were calculated
at the local maxima of the left frontal and temporal regions in
Fig. 3B. In the normal group, a repeated measures ANOVA with
two factors [Condition (active, passive, scrambled)  Region(LPMC, opercular/triangular, orbital, temporal)] revealed a strong
main effect of condition [F(2,12) = 20, P = 0.0002] and a marginal
main effect of region [F(3,18) = 3.2, P = 0.050], with no interaction [F(6,36) = 1.4, P = 0.23]. According to a Bonferroni/Dunn
test, the signal changes in the left LPMC were significantly higher
under the scrambled than under the active sentence condition(corrected P 5 0.05) (Fig. 3C). The signal changes in the opercular/triangular parts of the left F3, as well as those in the orbital
part of the left F3, were significantly higher under the passive andscrambled than under the active sentence conditions (Fig. 3D andE), whereas those in the left posterior superior/middle temporalgyri were significantly higher under the scrambled than under theactive and passive sentence conditions (Fig. 3F). These modulation
patterns of activations precisely replicated our previous results inthe normal participants (Kinno et al., 2008), indicating that these
crucial regions work in concert to process sentences, with theirrespective contributions being dynamically regulated by syntactic
requirements.
In the Other group, a strong main effect of condition[F(2,12) = 33, P 5 0.0001], as well as a marginal main effect of

For the normal group, we first examined the Twoargument Control contrast (Fig. 4A and Supplementary Table2), which reproduced significant activation in the bilateral regionsof the LPMC and posterior superior/middle temporal gyri, as wellas in the orbital part of the left F3, presupplementary motor area,
left posterior middle/inferior temporal gyri, and left intraparietal
sulcus. The absence of significant activation in the right intraparietal sulcus and precuneus, etc., which was present in Fig. 2A, may
be because of a reduced sensitivity of signal-to-noise ratio for thesmaller population and/or a longer (i.e. less demanding) intratrial
interval.
Even in the more stringent contrast of Two-argument Oneargument, all participants in the normal group showed consistently
left-lateralized activation in the left frontal and temporal regions(Fig. 4B). Notable idiosyncrasy was restricted to weaker activationin the right frontal and presupplementary motor area for a few
participants. Moreover, the group analysis in this contrast showed
clearly left-lateralized activations in the left frontal and temporal
regions (Fig. 5A and Supplementary Table 3), consistent with theresult shown in Fig. 2C. It is notable that significant activation was
absent in the right or medial regions in the normal brain.

Abnormal overactivity in the LPMC
group for correct responses alone
In the Two-argument – One-argument contrast, the LPMC group
showed activation in the left frontal (ventrally expanded) and temporal regions, as well as additional activation in the left intraparietal sulcus, right frontal regions, presupplementary motor area,
and right temporal regions (Fig. 5B). It is striking to note that
this activation pattern was similar to that in the normal brain for
the Two-argument Control contrast (Fig. 4A). Moreover, theseregions were consistently observed for all patients in the LPMC
group (Fig. 6A). The only notable idiosyncrasies were theenhanced activation in the right ventral frontal regions forPatient 1, and the absence of activation in the left intraparietal
sulcus for Patient 3. As the observed abnormal overactivity in the
LPMC group was selective to correct responses, these compensatory changes were coupled with task demands under the twoargument conditions.

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

Fig. 1A for the tasks), which exhibited activation in all of theregions mentioned above, together with lingual gyrus and cerebellar nuclei.
To set the same sensitivity of signal-to-noise ratio for functional
MRI maps of all tested groups, we equated the number of participants in each group. According to a Dunnett test, there was no
significant difference in age and laterality quotient of handedness
between the normal group (laterality quotient, 93  13.0) andeach patient group (corrected P 4 0.4). All of the following functional MRI results were based on the present data taken from thenormal group and patient groups.

1202| Brain 2014: 137; 1193–1212

R. Kinno et al.

covariance with two factors [Group (LPMC, F3, Other)  Condition (active, passive, scrambled)], which were projected in three orthogonal planes (sagittal, coronal, and axial from the top left panel) and onto the left surface-rendered standard brain. There was asignificant main effect of group in the left frontal regions (A), as well as a significant interaction of group by condition in the four left
regions (B). See Supplementary Table 2 for the stereotactic coordinates of the activation foci. Each yellow dot indicates the local maximumof an activated region. (C–F) Histograms for the per cent signal changes at the local maxima of the left LPMC (C), opercular/triangularparts of the left F3 (D), orbital part of the left F3 (E), and left posterior superior/middle temporal gyri (F). The per cent signal changes forthe active, passive, and scrambled sentence conditions are shown with reference to the one-argument condition (all trials). Error bars
indicate the standard error of the mean for the participants, and asterisks denote the significant differences among the two-argument
conditions (corrected P 5 0.05). Note that the activation modulation in the four regions, which was observed in the normal and Other
groups, was completely lost for the LPMC and F3 groups, i.e. agrammatic patients. A = active sentence condition; F3O = orbital part of
the F3; F3op/F3t = opercular/triangular parts of the F3; L = left; P = passive sentence condition; pSTG/MTG = posterior superior/middle
temporal gyri; S = scrambled sentence condition.

Abnormal overactivity in the F3 groupfor both correct and incorrect responses
In the F3 group, it is striking to note that the Twoargument One-argument contrast induced no significant activation in the whole brain (Fig. 5C). These radically differential

activation patterns between the LPMC and F3 groups indicate
distinct abnormal changes in activation. In contrast, the activation
pattern in the Other group (Fig. 5D) was similar to that in thenormal group. Indeed, all patients in the Other group showed
consistent activation in mostly the left frontal and temporal regions (Fig. 6B). Notable idiosyncrasy was restricted to the

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

Figure 3 Differential activation modulation among the two-argument conditions. (A and B) Significant regions identified by an analysis of

Reorganization by a left frontal glioma

Brain 2014: 137; 1193–1212| 1203

Multiple regions with abnormal
overactivity or underactivity

Figure 4 Syntax-related activation patterns in the normalgroup. (A) Significant regions identified by the Twoargument Control contrast (paired t-test) for the normalgroup, which were projected onto the left and right lateral surfaces of the standard brain. Medial sections are also shown. See
Supplementary Table 2 for the stereotactic coordinates of theactivation foci. (B) Significant regions identified by the Twoargument One-argument contrast for each participant in thenormal group. Activations were projected onto the left and rightlateral surfaces of each individual brain. Individual medial sections are also shown. The threshold was set at P 5 0.005 for thevoxel level. All normal participants showed consistent activationin the left frontal and temporal regions. L = left.

To precisely quantify overactivity and/or underactivity in the
LPMC and F3 groups, the per cent signal changes in the Twoargument One-argument contrast were calculated at the local
maxima of the 14 regions shown in Fig. 5 (yellow dots), all ofwhich were included in the regions revealed by the Picture–
sentence matching Control contrast for the normal participants(Fig. 2D). As some activated regions were more focal andenhanced in the patient groups, we took some regions from thepatients’ activation maps, all of which were clearly outside thelesions: the left frontal and temporal regions from the normalgroup (Fig. 5A and Supplementary Table 3), the left intraparietal
sulcus, right frontal regions, presupplementary motor area, righttemporal regions from the LPMC group (Fig. 5B and
Supplementary Table 3), and the left angular gyrus, lingual
gyrus, cerebellar nuclei from the F3 group (Fig. 5G and
Supplementary Table 4).
For each region with increased activation in the LPMC group, aDunnett test between the LPMC and normal groups showed significant overactivity (corrected P 5 0.05; red asterisks in Fig. 7A).
For each region with decreased activation in the F3 group, aDunnett test between the F3 and normal groups showed

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014activation in the right hemisphere for some patients, which wasmuch less prominent than that in the LPMC group.
The absence of significant activation in the F3 group may havebeen because of comparable activations between the two-argument and one-argument conditions, or those between correct andincorrect responses. The Two-argument One-argument contrast
was thus further examined separately for trials with correct andincorrect responses under the two-argument conditions. In thenormal group, the activation pattern for correct responses alone(Fig. 5E and Supplementary Table 4) exactly replicated that shownin Fig. 5A; there was no significant activation for incorrect responses in the whole brain (Fig. 5F). In contrast, we observed
unique activation patterns in the F3 group, such that the left
LPMC, left angular gyrus, lingual gyrus, and cerebellar nuclei
showed consistent overactivity for both correct (Fig. 5G) and incorrect (Fig. 5H) responses. These activation patterns for bothcorrect and incorrect responses were consistently observed for all
patients in the F3 group (Fig. 6C and D). Notable idiosyncrasy was
restricted to activation in the right parietal and occipital regions forPatient 9, and to activation in the presupplementary motor areafor Patient 10; the activation patterns for correct and incorrect
responses were exactly the same even for these two patients.
Here we should note that both behavioural and activation data
were basically consistent among the patients within each of thepatient groups (Table 2 and Fig. 6), excluding any modulation as aresult of tumour grade and/or gender. Therefore, tumour location
was the only factor that differentiated the three patient groups.
Moreover, our results indicate that the activation patterns wereboth quantitatively and qualitatively different between the LPMC
and F3 groups, particularly with respect to the activations for correct and incorrect responses.

1204| Brain 2014: 137; 1193–1212

R. Kinno et al.

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

Figure 5 Differential activation patterns in the patient groups. (A–D) Significant regions identified by the Two-argument Oneargument contrast for the normal group (A), LPMC group (B), F3 group (C), and Other group (D). See Supplementary Table 3 for thestereotactic coordinates of the activation foci. The normal and Other groups showed clearly left-lateralized activation. In contrast, the
LPMC group showed overactivity in both hemispheres, whereas the F3 group showed no significant activation at all. (E and F) Significant
regions identified by the Two-argument One-argument contrast for the normal group, shown separately for correct (E) and incorrect (F)responses. See Supplementary Table 4 for the stereotactic coordinates of the activation foci. (G and H) Significant regions identified by the(continued)

Reorganization by a left frontal glioma
significant underactivity (green asterisks in Fig. 7A) except the left
posterior middle/inferior temporal gyri. In the F3 group, it is striking to note that the signal changes for correct and incorrect responses were exactly the same in all 14 of the regions examined(Fig. 7B). For each region with increased activation in the F3 group(i.e. left LPMC, left angular gyrus, lingual gyrus, and cerebellar
nuclei), a Dunnett test between the F3 and normal groups
showed significant overactivity for both correct and incorrect responses (black asterisks in Fig. 7B). In contrast, the other left
frontal and temporal regions, including the left posterior middle/
inferior temporal gyri, showed significant underactivity for correct
responses (green asterisks in Fig. 7B).

Based on the functional connectivity among the 14 syntax-related
regions, we identified normal networks with direct connections byusing a partial correlation method (Smith, 2012). Figure 7C showsa partial correlation matrix and network-boundary effects, whichclearly revealed three separated networks in the normal brain. We
confirmed that there was no negative correlation within individual
networks. The non-diagonal correlations within individual networks were significantly greater than those between any of two
networks (P 5 0.0001; see the bar graph in Fig. 7C). We named
the largest network as Network I, the second-largest and morewidespread network as Network II, and the remaining network as
Network III (Fig. 7D).
Network I consists of the opercular/triangular parts of the left
F3, left intraparietal sulcus, right LPMC, opercular/triangular partsof the right F3, presupplementary motor area, and right posterior
superior/middle temporal gyri, all of which were overactivated in
the LPMC group selective for correct responses. Network II consists of the left LPMC, left angular gyrus, lingual gyrus, and cerebellar nuclei, all of which were overactivated in the F3 group forboth correct and incorrect responses. Network III consists of thetriangular part of the left F3, orbital part of the left F3, left posterior superior/middle temporal gyri, and left posterior middle/inferior temporal gyri, all of which were underactivated in the F3group for correct responses. These results clearly demonstrate thatthe three networks are functionally distinct in the normal circuitry
subserving syntactic processing (Table 3). Moreover, it is striking
to note that these normal networks were differentially recruited by
the LPMC and F3 groups leading to abnormal overactivity andunderactivity.

| 1205

Anatomical connections for thesyntax-related networks
We used diffusion tensor imaging with probabilistic tractography
in the normal brain to further examine which of the dorsal andventral tracts are actually integrated into each of these syntaxrelated networks. Seed masks were placed at the 14 regions in
the following nine pairs: the opercular/triangular parts of the left
F3 and those of the right F3; the opercular/triangular parts of theleft F3 and left intraparietal sulcus; the left intraparietal sulcus andright posterior superior/middle temporal gyri; the opercular/triangular parts of the right F3 and right posterior superior/middle
temporal gyri; the opercular/triangular parts of the right F3 andpresupplementary motor area; the left LPMC and left angulargyrus; the left LPMC and cerebellar nuclei; the left angulargyrus and lingual gyrus; and the triangular part of the left F3
and left posterior middle/inferior temporal gyri. The tracts connecting other pairs of regions were basically covered by the examined pairs.
We identified a single largest cluster that connected two regions, together with much smaller clusters or islands (Fig. 8A–C).
For Network I, the anterior corpus callosum connected the opercular/triangular parts of the left F3 and those of the right F3,
whereas the splenium of corpus callosum connected the left intraparietal sulcus and right posterior superior/middle temporal gyri.
Our tractography did not consistently connect the opercular/
triangular parts of the left F3 and the left intraparietal sulcus.
On the other hand, the right dorsal pathway of the arcuate andsuperior longitudinal fasciculi connected the right frontal and temporal regions. The opercular/triangular parts of the right F3 werealso directly connected to the presupplementary motor area. For
Network II, the left dorsal pathway of the arcuate and superior
longitudinal fasciculi connected the left LPMC and left angulargyrus, and reached the left posterior superior/middle temporalgyri. Moreover, the left LPMC and cerebellar nuclei were connected via the thalamus, whereas the left angular gyrus and lingual gyrus were directly connected. For Network III, the left
ventral pathway of the middle longitudinal fasciculus and extreme
capsule connected the left ventral frontal and posterior temporal
regions, and reached the left angular gyrus. It is notable that mostof these anatomical connections, especially those within Network
II, were consistent with the higher values of partial correlations in
the functional connectivity (Fig. 7C). These results are summarized
in Fig. 8D, and further demonstrate that the wide-ranging fibres of
the three networks were also anatomically distinct and penetrated
both the cerebral hemispheres and left cerebellum.

Figure 5 Continued
Two-argument One-argument contrast for the F3 group, shown separately for correct (G) and incorrect (H) responses. Note theconsistent overactivity in the left LPMC, left angular gyrus, lingual gyrus, and cerebellar nuclei for both correct and incorrect responses.
Each yellow dot represents the local maximum of an activated region used for the later analyses. AG = angular gyrus; F3O = orbital part of
the F3; F3op/F3t = opercular/triangular parts of the F3; F3t = triangular part of the F3; IPS = intraparietal sulcus; L = left; LG = lingual
gyrus; n. = nuclei; pMTG/ITG = posterior middle/inferior temporal gyri; pre-SMA = presupplementary motor area; pSTG/MTG = posterior
superior/middle temporal gyri; R = right.

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Three networks of regions based on thefunctional connectivity

Brain 2014: 137; 1193–1212

1206| Brain 2014: 137; 1193–1212

R. Kinno et al.

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Figure 6 Consistently activated regions within each patient group. (A and B) Significant regions identified by the Two-argument Oneargument contrast for each patient in the LPMC group (Patients 1–7, see Table 1) (A), and in the Other group (Patients 15–21) (B).
Activations were projected onto the left and right lateral surfaces of each individual brain. Individual medial sections are also shown. The
threshold was set at P 5 0.005 for the voxel level. All patients in the LPMC group showed consistent activation in the bilateral frontal andtemporal regions, as well as in the presupplementary motor area and left intraparietal sulcus. In contrast, all patients in the Other group
showed consistent activation in mostly the left frontal and temporal regions, as in the normal group (Fig. 5A). (C and D) Significant regions
identified by the Two-argument One-argument contrast for each patient in the F3 group (Patients 8–14), shown separately for correct(C) and incorrect (D) responses. All patients in the F3 group showed consistent overactivity in the left LPMC, left angular gyrus, lingual
gyrus, and cerebellar nuclei for both correct and incorrect responses. L = left.

Reorganization by a left frontal glioma

Brain 2014: 137; 1193–1212| 1207

argument One-argument contrast at the local maxima of 14 regions. Error bars indicate the standard error of the mean for theparticipants. Asterisks denote significant differences from the normal group (corrected P 5 0.05), which consisted of either overactivity inthe LPMC group or underactivity in the F3 group. (B) Per cent signal changes in the Two-argument One-argument contrast, shown
separately for correct and incorrect responses in the F3 and normal groups. For the correct responses, asterisks denote significant
differences between the two groups (corrected P 5 0.05), indicating overactivity for correct and incorrect responses, as well as underactivity for correct responses. (C) A partial correlation matrix and network-boundary effects for the normal participants. The 14 regions
were arranged in the order of Fig. 7A and B, except the left LPMC and opercular/triangular parts of the left F3. As in Fig. 7A and B, thethree networks surrounded by red, green, and blue boxes correspond to the Networks I, II, and III, respectively. (D) Schematic of the three
syntax-related networks in the normal brain. The brain regions for Networks I, II, and III are shown in red, green, and blue, respectively.
Note that these three networks involve the opercular/triangular parts of the left F3, left LPMC, and orbital part of the left F3 in the left
frontal cortex, respectively (Table 3). Connections within individual networks are shown with coloured lines. AG = angular gyrus;
F3O = orbital part of the F3; F3op/F3t = opercular/triangular parts of the F3; F3t = triangular part of the F3; IPS = intraparietal sulcus;
L = left; LG = lingual gyrus; n. = nuclei; pMTG/ITG = posterior middle/inferior temporal gyri; pre-SMA = presupplementary motor area;pSTG/MTG = posterior superior/middle temporal gyri; R = right.

Discussion
We established two qualitatively different types of agrammatic
comprehension, depending on glioma location (Fig. 1C and D).
By comparing the activation patterns among the three patientgroups and the normal group, we observed that the activation
modulation under the two-argument conditions was completely
lost in the syntax-related regions in the LPMC and F3 groups(Fig. 3C–F). Moreover, we found dramatic changes in theactivation patterns for both groups (Fig. 5), which accompanied
abnormal overactivity and/or underactivity in the syntax-related
regions (Fig. 7A and B). Furthermore, by examining functional
connectivity in the normal brain, we identified three syntax-related
networks among those regions (Fig. 7C), and anatomically visualized connections within individual networks (Fig. 8). These results
demonstrate that agrammatic comprehension is associated withthe global reorganization of functionally distinct networks,
which indeed reflects a differential change in the relative

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

Figure 7 Abnormal overactivity and/or underactivity of the three syntax-related networks. (A) Per cent signal changes in the Two-1208

| Brain 2014: 137; 1193–1212

R. Kinno et al.

Table 3 Summary of the normal and abnormal activity in each syntax-related network
Network

I
II
III

Left frontal
regions included
L. F3op/F3t
L. LPMCL. F3t, L. F3O

Two-argument

ControlTwo-argument

One-argument

Possible normal functions

Normal group

Normal group

LPMC group

F3 group

+

+



+

++

+

–
++
–

Syntax and its supportive system
Syntax and input/output interface
Syntax and semantics

Significant activations are schematically presented: + , normal activity as a whole network; + + , abnormal overactivity as a whole network; –, abnormal underactivity as
a whole network; , significant activity in the left frontal regions alone, which was dependent on the reference conditions (control or one-argument; see Network I).
See Fig. 4A for the Two-argument Control contrast; see Fig. 5A, B, D, and G for the Two-argument One-argument contrast.
F3O = orbital part of the F3; F3op/F3t = opercular/triangular parts of the F3; F3t = triangular part of the F3; L = left.

frontal lesions including these regions (Krainik et al., 2003).
Taken together, Network I would be related to syntax and its
supportive system.
Network II included the left dorsal pathway connecting the left
LPMC and left angular gyrus, and this pathway reached the left
posterior superior/middle temporal gyri. A previous study has suggested that the dorsal pathway connecting the LPMC and temporal gyrus is important for ‘sensory to motor mapping’ rather
than for ‘semantic and syntactic functions’ (Friederici, 2011).
Although the functional role of the dorsal pathway remains controversial, this and other pathways included in Network II may
function as the interface between sensory input and motor
output. As shown in Fig. 8B, the connection between the left
angular gyrus and lingual gyrus was also included in Network II.
Previous studies have reported significant activation in the left
angular gyrus for searching the semantic features of visually presented words and pictures (Seghier et al., 2010), and that in the
lingual gyrus for memorizing visually presented phrases(Hashimoto and Sakai, 2002). A previous PET study with dyslexicpatients has shown the importance of the functional connectivity
between these two regions for single-word reading abilities(Horwitz et al., 1998), suggesting the significance of this pathway
for the visual input. Network II further included the connection
between the left LPMC and cerebellar nuclei via the thalamus. The
deep cerebellar nuclei has been considered a part of a widespread
network including the frontal regions and thalamus for motor outputs (Habas, 2010), which has been anatomically confirmed(Behrens et al., 2003; Granziera et al., 2009). The resection of
cerebellum tumours located in the midline often causes posterior
fossa syndrome, including cognitive deficits and cerebellar mutism,i.e. speech output problems (Mariën et al., 2013). It has beenreported that a lesion in the dentate-thalamo-cortical tract leads
to cerebello-cerebral diaschisis and subsequent mutism (Küper andTimmann, 2013). Taken together, Network II would be related tosyntax and input/output interface for linguistic processing.
Network III consisted of the triangular/orbital parts of the left
F3 and left posterior superior/middle/inferior temporal gyri.
Previous studies have suggested that the orbital part of the left
F3 is selectively involved in the semantic processing of sentences(Homae et al., 2002), and that the left posterior superior/middle
temporal gyri are related to both syntactic and semantic processing (Friederici et al., 2003; Suzuki and Sakai, 2003; Kinno et al.,2008). These findings suggest that the left ventral frontal andDownloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

contribution of these three networks to normal syntax-related
functions (Table 3).
Based on the overview of cognitive and neuronal (i.e. anatomical) changes by Price and Friston (1999), it is probable that abnormal overactivity of Network I in the LPMC group reflected a
cognitive change. This overactivity suggests a compensatorychange in cognitive architecture because of the increased demands
on normal processes, because the overactivity mimicked the activation pattern in the Two-argument Control contrast (i.e. contrasted with the lower control) in the normal group (Fig. 4A). As
regards Network II, signal changes for correct responses were
exactly the same as those for incorrect responses in the F3group (Fig. 7B), independent of cognitive factors, suggestingthat its overactivity reflected a neuronal change due to the disconnection or disinhibition of a duplicate (degenerate) system. The
underactivity of Network III in the F3 group indicates anotherneuronal change leading to diaschisis in the left temporal cortex.
The functions of these three networks can be inferred by integrating the possible roles of individual regions suggested by previous studies into the present results. First of all, thecomplementary overactivity observed between the left LPMC
and the opercular/triangular parts of the left F3 (Table 3) is consistent with the critical roles of both regions in syntactic processing(Hashimoto and Sakai, 2002; Bornkessel et al., 2005; Kinno et al.,2008, 2009). Network I included not only the opercular/triangularparts of the left F3, but right frontal and temporal regions. A
previous study has reported functional roles of right frontal andtemporal regions for repairing syntactically incorrect sentences(Meyer et al., 2000), suggesting that these right regions would
be related to a supportive system for syntactic processing. As
shown in Fig. 8A, the right dorsal pathway of the arcuate andsuperior longitudinal fasciculi connected the right frontal and temporal regions. It has been suggested that this right pathway isimportant for an artificial rule learning of pitch patterns (Louiet al., 2011), which may also have a supportive role for processing
linguistic stimuli. As regards the posterior corpus callosum between
the left intraparietal sulcus and right superior/middle temporalgyri, previous studies have reported its functional roles for utilizing
prosodic cues supportively for processing auditory presented sentences (Friederici et al., 2007). The presupplementary motor area,
as well as the pathway between the presupplementary motor area
and right LPMC may have another supportive role, as suggested
by transient speech disorders due to a resection of the medial

Reorganization by a left frontal glioma

Brain 2014: 137; 1193–1212| 1209Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

Figure 8 Anatomical connections for the syntax-related networks in the normal brain. Based on the results with diffusion tensor imaging,
the population probability maps are shown on the left/right lateral, anterior/posterior, and dorsal surfaces of the standard brain withmaximum intensity projection. Red, green, and blue spheres represent seed masks for Networks I, II, and III, respectively. The pathways
have been thresholded to show only those present in at least 5 of 11 participants; the colour scale denotes the number of participants.
(A–C) Fibres within Networks I (A), II (B), and III (C). (D) Perspective drawings of the three syntax-related networks. Fibres for two of
Networks I (red), II (green), and III (blue) are shown in the left lateral, posterior, and dorsal views of the standard brain. More lateral,posterior, and dorsal fibres are shown in the upper layers of each panel. AG = angular gyrus; F3op/F3t = opercular/triangular parts of theF3; F3t = triangular part of the F3; IPS = intraparietal sulcus; L = left; LG = lingual gyrus; n. = nuclei; pMTG/ITG = posterior middle/inferior
temporal gyri; pre-SMA = presupplementary motor area; pSTG/MTG = posterior superior/middle temporal gyri; R = right.

1210| Brain 2014: 137; 1193–1212

on the normalized brains, without relying on topographical landmarks. In future studies, other methods such as surface-based
semi-automated parcellation procedures would be helpful for the
anatomical identification of regions on an individual’s cortical map(Fischl et al., 2004).
A previous functional MRI study on patients with the non-fluent
variant of primary progressive aphasia (both the frontal and temporal regions were widely damaged) reported abnormal overactivity in the left posterior inferior frontal gyrus, as well as the absenceof activation modulation due to ‘syntactic complexity’ (Wilson
et al., 2010), which seems consistent with our findings in the
LPMC group. However, normal modulation was observed in theleft mid-superior temporal sulcus in their patients in spite of thetemporal lesions, which may reflect differences in other cognitive
factors among the compared conditions. In our LPMC and F3
groups, the normal modulation in the left posterior superior/
middle temporal gyri was clearly lost. It is likely that the loss ofactivation modulation in the left frontal regions was transmitted toother intact temporal regions in Network III.
According to the above discussion, the homotopic reorganization would have been the result of cognitive change, which wasobserved for all patients in the LPMC group, but not for any
patients in the F3 or Other group (Fig. 6). A previous study hasreported that aphasic patients showed overactivity in the rightfrontal regions for an auditory comprehension task, but only inthe subacute phase after stroke (2 weeks) (Saur et al., 2006).
Regarding the question of why homotopic reorganization occurs ina subset of patients (Desmurget et al., 2007), our study suggeststhat homotopic reorganization actually depends on the location of
a lesion. The differentiation and identification of the three syntaxrelated networks reported here would be a first step toward theelucidation of intricate networks uniquely specialized in the humanbrain for language processing.

Acknowledgements
We thank N. Komoro for technical assistance and H. Matsuda foradministrative assistance.

Funding
This work was supported by a Core Research for Evolutional
Science and Technology (CREST) grant from the Japan Scienceand Technology Agency (JST) and by a Grant-in-Aid for
Scientific Research (S) (No. 20220005) / a Grant-in-Aid forYoung Scientists (B) (No. 24720190) from the Ministry of
Education, Culture, Sports, Science, and Technology of Japan.

Supplementary material
Supplementary material is available at Brain online.

Downloaded from http://brain.oxfordjournals.org/ at University of Tokyo on April 2, 2014

posterior temporal regions in Network III would be related toboth syntax and semantic processing. As shown in Fig. 8C, theventral route connected the triangular part of F3 and temporalgyrus. A previous lesion study has reported that the selective
disconnection in this pathway caused deficits in sentence comprehension while lexical processing was left intact (Griffiths et al.,2013). Taken together, Network III would be related to thesyntax-semantic interaction. These three syntax-related networks
with distinct functional roles in the normal brain thus separately
connect key linguistic centres and work in concert to processsentences.
The patients in the LPMC group showed agrammatic comprehension (Fig. 1C), even though all of the regions of Network I
were overactivated, whereas activations in Networks II and III remained normal (Fig. 7A). It is possible that such an increased
contribution of the supportive system of network I could help
partially overcome the deficit in the comprehension of passive
sentences, which are marked in the verb morphology (-areru)
and thus more salient than scrambled sentences. In contrast, thepatients in the F3 group showed a different type of agrammatic
comprehension (Fig. 1D), when all of the regions of Network III,
as well as the opercular/triangular parts of the left F3 (i.e. themost crucial region that controls the other regions in Network I),
were underactivated for correct responses (Fig. 7B). This deficit
can be explained by noting that these patients failed to use thesupportive function and syntax-semantic interaction, even thougha neuronal change led to an increased contribution of the input/
output interface of Network II. Therefore, the two types of agrammatic comprehension are consistent with the relative contributionof the three syntax-related networks.
In the neuropsychological literature, an opposing argument has
been made that the study of brain tumours may not allow valid
conclusions on the functional localization for the following reasons(Karnath and Steinbach, 2011). First, the diffuse spread of atumour might extend beyond the tumour region as visualized by
T1-weighted or T2-weighted MRI. In the present study, however,each lesion determined by MRI agreed well with the metabolicchanges measured by 11C-methionine, 18F-fluorodeoxyglucose,and 11C-choline PET (Kato et al., 2008). Second, normal function
can be well preserved in the tumour region because of infiltration
rather than destruction (Ojemann et al., 1996; Krainik et al.,2003). This point was not a problem in the present study, as we
examined the whole brain, including the tumour region. We
observed apparently normal left LPMC activations in the LPMC
group (Fig. 7A), which could be a result of compensatory overactivation of remaining cells. Moreover, a brain shift caused by themass effect of the tumour, if any, is limited to local activation
changes, which cannot explain the global overactivity or underactivity. As a tumour may alter topographical landmarks of sulci,
gyri, and ventricles, the location of a glioma could be estimatedonly approximately; we used a voxel-based approach of theAnatomical Automatic Labelling on normalized structural images
for the approximate tumour location (Table 1). It is also difficult todelineate cortical areas even in normal participants because of thehigh variability of the gyrification in the inferior frontal gyrus. Our
categorization criteria in the LPMC and F3 groups simply required
voxel-based overlaps between a tumour and functional MRI data

R. Kinno et al.

Reorganization by a left frontal glioma

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Supplementary material

Differential reorganization of three syntax-related networks
induced by a left frontal glioma

Ryuta Kinno, Shinri Ohta, Yoshihiro Muragaki, Takashi Maruyama, andKuniyoshi L. Sakai

Contents:
Supplementary Table 1 ································································································· 2
Supplementary Table 2 ································································································· 3
Supplementary Table 3 ································································································· 4Supplementary Table 4 ································································································· 5Supplementary materials and methods ·········································································· 6Supplementary references ··························································································· 16

Supplementary Table 1 Activation of the syntax-related regions in normal participants (n = 14)(Kinno et al., 2008)
Brain region

BA

Side

x

y

z

Z

6/8

L

–45

9

33

5.3

R

42

6

36

4.3

L

–48

27

15

3.9

R

48

15

36

3.9

Two-argument – Control
LPMC
F3op/F3t

44/45

F3t

45

L

–54

30

9

3.9

F3O

47

L

–45

45

–3

3.4

R

33

21

3

4.6

pre-SMA

6/8

M

9

18

24

5.1

pSTG/MTG

22/21

L

–54

–54

3

5.4

R

57

–54

9

4.3

pMTG/ITG

37/19

L

–42

–78

–12

4.5

IPS

7/39/40

L

–27

–72

45

5.0

R

33

–72

45

4.1

L

–36

–57

39

4.1

R

39

–63

45

4.6

M

0

–75

45

5.0

L

–39

0

45

5.1

R

42

3

36

3.6

L

–63

–48

9

5.5

R

54

–63

6

4.3

L

–24

–78

33

4.2

AG/SMG

39/40

Precuneus
One-argument – Control
LPMC
pSTG/MTG
IPS

6/8
22/21
7/39/40

Two-argument – One-argument
LPMC

6/8

L

–39

3

33

5.0

F3op/F3t

44/45

L

–45

15

27

4.5

F3t

45

L

–51

39

12

3.1

F3O

47

L

–51

21

–6

3.5

pSTG/MTG

22/21

L

–57

–51

6

5.1

pMTG/ITG

37/19

L

–51

-69

–6

4.1

Stereotactic coordinates (x, y, z) in the Montreal Neurological Institute space are shown for eachactivation peak of Z values. The threshold was set at corrected P < 0.05 for the cluster level.
AG/SMG = angular gyrus/supramarginal gyrus; BA = Brodmann’s area; F3O = orbital part of the F3;
F3op/F3t = opercular/triangular parts of the F3; F3t = triangular part of the F3; IPS = intraparietal sulcus;
L = left; M = medial; pMTG/ITG = posterior middle/inferior temporal gyri; pre-SMA = pre-supplementary
motor area; pSTG/MTG = posterior superior/middle temporal gyri; R = right.

2Supplementary Table 2 Activation of the syntax-related regions in the patient groups andnormal participants (n = 7)
Brain region

BA

Side

x

y

z

Z

Analysis of covariance with two factors (group × condition)
Main effect of group
F3op/F3t

44/45

L

–51

15

15

4.0

F3O

47

L

–39

21

–3

3.2

Interaction of group by condition
LPMC

6/8

L

–42

3

48

5.4

F3op/F3t

44/45

L

–54

15

15

4.6

F3O

47

L

–39

21

–3

4.9

pSTG/MTG

22/21

L

–54

–45

3

4.2

6/8

L

–42

–6

42

4.0

R

48

9

42

4.2

Two-argument – Control
Normal group
LPMC
F3op/F3t

44/45

L

–45

21

24

3.4

F3t

45

L

–51

33

3

3.0

F3O

47

L

–39

18

–3

3.2

pre-SMA

6/8

M

6

24

48

3.8

pSTG/MTG

22/21

L

–54

–48

3

3.5

R

51

–57

9

3.2

pMTG/ITG

37/19

L

–45

–69

–9

4.1

IPS

7/39/40

L

–38

–54

51

4.1

The threshold was set at corrected P < 0.05 for the cluster level.
BA = Brodmann’s area; F3O = orbital part of the F3; F3op/F3t = opercular/triangular parts of the F3; F3t= triangular part of the F3; IPS = intraparietal sulcus; L = left; M = medial; pMTG/ITG = posterior
middle/inferior temporal gyri; pre-SMA = pre-supplementary motor area; pSTG/MTG = posterior
superior/middle temporal gyri; R = right.

3Supplementary Table 3 Regions identified by Two-argument – One-argument for each group
Brain region

BA

Side

x

y

z

Z

LPMC

6/8

L

–48

3

42

3.9

F3op/F3t

44/45

L

–45

18

27

3.3

F3t

45

L

–48

33

6

4.3

F3O

47

L

–36

15

–6

2.8

pSTG/MTG

22/21

L

–57

–48

0

3.4

pMTG/ITG

37/19

L

–45

–69

0

4.4

6/8

L

–39

6

48

3.4

R

30

3

45

3.3

L

–57

18

24

3.1

R

33

18

24

3.8

Normal group

LPMC group
LPMC
F3op/F3t

44/45

F3t

45

L

–57

24

15

4.5

F3O

47

L

–36

27

–12

3.6

pre-SMA

6/8

M

9

24

51

3.2

pSTG/MTG

22/21

L

–51

–39

3

4.2

R

60

–57

3

2.8

pMTG/ITG

37/19

L

–57

–60

0

3.1

IPS

7/39/40

L

–21

–72

51

4.0

LPMC

6/8

L

–48

6

42

4.2

F3op/F3t

44/45

L

–39

18

27

4.5

F3t

45

L

–39

39

6

3.6

F3O

47

L

–39

33

–3

5.2

pSTG/MTG

22/21

L

–57

–48

0

3.6

pMTG/ITG

37/19

L

–51

–75

3

3.0

F3 group
None
Other group

The threshold was set at corrected P < 0.05 for the cluster level.
BA = Brodmann’s area; F3O = orbital part of the F3; F3op/F3t = opercular/triangular parts of the F3; F3t= triangular part of the F3; IPS = intraparietal sulcus; L = left; M = medial; pMTG/ITG = posterior
middle/inferior temporal gyri; pre-SMA = pre-supplementary motor area; pSTG/MTG = posterior
superior/middle temporal gyri; R = right.

4Supplementary Table 4 Regions identified by Two-argument – One-argument separately forcorrect and incorrect responses
Correct
Brain region

Incorrect

BA

Side

x

y

z

Z

LPMC

6/8

L

–51

9

42

3.1

F3op/F3t

44/45

L

–39

18

24

3.6

F3t

45

L

–42

39

3

4.1

F3O

47

L

–36

15

–3

2.8

pSTG/MTG

22/21

L

–54

–39

0

3.7

pMTG/ITG

37/19

L

–48

–69

3

3.2

LPMC

6/8

L

–45

9

39

AG

39

L

–33

–60

LG

18

M

–3

x

y

z

Z

3.9

–42

6

39

3.0

18

3.8

–33

–66

21

3.1

–69

6

3.3

0

–75

0

3.0

9

–87

3

3.5

–3

–51

–27

4.3

0

–54

–30

4.2

Normal group

F3 group

cerebellar nuclei

M

The threshold was set at corrected P < 0.05 for the cluster level. In the Normal group, there was no
significant activation for incorrect responses in any regions. In contrast, the F3 group showed an exact
correspondence between the activated regions for correct and incorrect responses.
AG = angular gyrus; BA = Brodmann’s area; F3O = orbital part of the F3; F3op/F3t =opercular/triangular parts of the F3; F3t = triangular part of the F3; L = left; LG = lingual gyrus; M =medial; pMTG/ITG = posterior middle/inferior temporal gyri; pre-SMA = pre-supplementary motor area;pSTG/MTG = posterior superior/middle temporal gyri.

5Supplementary materials and methods
Here, we provided a full description of the experimental conditions and the procedures
used to acquire the data presented.
Participants
We tested 21 patients, who were native Japanese speakers newly diagnosed ashaving a left frontal glioma (Table 1). The patients preoperatively performed a
picture-sentence matching task (Fig. 1A), and underwent functional MRI scans in an
event-related design. The patients were divided into three groups based on theindividual tumor locations in the normalized brain (Fig. 1B): patients with a glioma in
the left LPMC (LPMC group, n = 7); patients with a glioma in the opercular/triangularparts of the left F3 (F3 group, n = 7); and patients with a glioma in the other left frontal
regions (Other group, n = 7). The categorization criterion of each group was whether
or not the glioma of a patient overlapped, at least partially on a voxel-by-voxel basis,
with functionally identified regions in our previous study (Kinno et al., 2008): the left
LPMC shown in Figure 3 of that paper, and the opercular/triangular parts of the left F3
shown in Figure 4 of that paper.
The patients reported here underwent surgery at the Department of
Neurosurgery, Tokyo Women’s Medical University, after behavioral and functional MRI
assessment at the University of Tokyo, Komaba. The following conditions comprised
the criteria for inclusion of 21 patients in the present study (Table 1): (i)right-handedness, (ii) no deficits in verbal/written communication or other cognitive
abilities reported by the patients or physicians, (iii) no history of neurological or
psychiatric disorders other than glioma and seizures, (iv) freedom from seizures withor without antiepileptic drugs, (v) no medical problems related to MRI acquisition, and(vi) completion of at least three functional MRI runs without significant head movement.
The types of antiepileptic drugs used were carbamazepine (400 mg/day), gabapentin(600 mg/day), phenobarbital (90-120 mg/day), phenytoin (200-300 mg/day), valproateacid (800-1200 mg/day), and zonisamide (200 mg/day). From our previous study(Kinno et al., 2009), we included seven patients who were scanned with the samefunctional MRI protocol as used in the present study.
The laterality quotient of handedness was determined by the Edinburgh
handedness inventory (Oldfield, 1971). The verbal/nonverbal intelligence quotient was
6assessed with the Japanese version of the Wechsler Adult Intelligence Scale-III (1997,
2006; Harcourt Assessment Inc., San Antonio, Texas, USA). Hemispheric dominancewas determined by amytal testing, in which the patient counted aloud to a certain
number with both hands raised, after an injection of amytal. As soon as contralateral
hemiplegia occurred, a picture naming task was performed to examine any speecharrest. The tumor type and grade were postoperatively and pathologically diagnosedby the World Health Organization Classification of Tumors of the Nervous System(2000).
In the present study, we recruited seven normal age-matched participants forfunctional MRI experiments [Normal group; five males and two females, aged 25-43
years, 31 ± 5.9 (mean ± standard deviation)]. Eleven normal age-matched participants(ten males and one female, aged 19-40 years, 29 ± 6.0) were also tested in the
diffusion tensor imaging studies. The four normal participants that took part in thefunctional MRI studies were a subset of the diffusion tensor imaging participants.
Written informed consent was obtained from each participant after the nature andpossible consequences of the studies were explained. Approval for the experiments
was obtained from the institutional review board of the University of Tokyo, Komaba,
as well as of the Tokyo Women’s Medical University.
Lesion analyses
The glioma was first identified on the normalized T1-weighted structural image, andthe glioma boundary was semi-automatically determined using the 3D Fill tool in
MRIcroN software (http://www.mccauslandcenter.sc.edu/mricro/mricron/), which
generated a contiguous cluster of voxels defined by the intensity of the glioma itself.
The boundary of each lesion, including brain edemas and abnormalities of perfusion,
was confirmed with T2-weighted MR images taken at the Department of Neurosurgery,Tokyo Women’s Medical University. The absence of any skip lesions distant from atumor was confirmed with 11C-methionine, [18F] fluorodeoxyglucose, and 11C-choline
PET data (resolution = 4.8 × 4.8 × 4.25 mm3) taken at the Chubu Medical Center for
Prolonged Traumatic Brain Dysfunction (Minokamo City, Gifu, Japan). Lesion overlap
maps (Fig. 1B), as well as each patient’s activated regions that were transformed backto the individual brains, were shown using MRIcroN software.

7

Stimuli
Each visual stimulus consisted of a picture with head symbols (○, □, or ∆) at the top,
and of an always grammatical sentence at the bottom (Fig. 1A). For each stimulus, wechose two different head symbols. The sentences describing actions were written
using a combination of the hiragana and kanji writing systems. In Japanese syntax, thegrammatical relations (“subject, direct object, or indirect object” in linguistic terms) are
first marked by grammatical particles (nominative, dative, or accusative), which in turnallow the assignment of semantic roles (“agent, experiencer, or patient” in linguistic
terms, i.e., an agent who initiates the action, and an experiencer/patient who isaffected by it), whereas passiveness is also marked in the verb morphology (-areru).
We used four kinds of grammatical particles, which represent the syntactic informationin Japanese: -ga, a nominative case marker; -ni, a dative case marker; -o, anaccusative case marker; and -to, a coordinator (and). Two sets of Japanese verbs (sixtransitive verbs: pull, push, scold, kick, hit, and call; and six intransitive verbs: lie, stand,walk, run, tumble, and cry) were used, each of which, including the passive forms, had
either four or five syllables. Note that the verb “call” is used only as a transitive verb inJapanese. There was no significant difference in frequency between the two sets ofverbs (t(10) = 0.7, P = 0.5), according to the Japanese lexical database (“Nihongo-no
Goitokusei” (Lexical Properties of Japanese), Nippon Telegraph and Telephone
Corporation Communication Science Laboratories, Tokyo, Japan, 2003). The
numbers of syllables and letters were strictly controlled among all conditions.
Using the same task, we tested two types of conditions with different sets ofstimuli: Two-argument and One-argument conditions. Under the Two-argument
conditions with an identical picture set, we tested three different sentence types: active,
passive, and scrambled sentences. Scrambled sentences are perfectly normal, notonly in Japanese but in German, Finnish, and other languages. Under the
Two-argument conditions, each sentence ended with a transitive verb, and had twoarguments (phrases associated with the predicate) with different grammatical relations
and semantic roles. More specifically, the active, passive, and scrambled sentences
corresponded to “subject and direct object” (agent and patient), “subject and indirect
object” (experiencer and agent), and “direct object and subject” (patient and agent)types, respectively.
Under the One-argument condition, each sentence ended with an intransitive
verb, and corresponded to a “double subjects” (double agents) type, which did not8involve two-argument relationships. A linguistically meaningful contrast is thus
“Two-argument – One-argument,” where we averaged together activations under the
Active, Passive, and Scrambled sentence conditions. This contrast mainly involved
syntactic processes, together with minimal semantic processes of semantic roleassignment (experiencer/patient) and lexico-semantics (verb types), whereas general
cognitive processes were well controlled. Under the Two-argument conditions, thenumber of lines used in each picture except the head symbols was 14 ± 2.4, whereas
under the One-argument condition, equally complex pictures (number of lines, 14 ±2.5) were used. Under both conditions, half of the pictures depicted actions occurringfrom left to right, and the other half depicted actions occurring from right to left (see Fig.
1A); head symbols were also counterbalanced for both sides. These pictures further
excluded the involvement of pragmatic information about word use (e.g., “An officer
chases a thief” is more acceptable than “A thief chases an officer”). There were 48
different stimuli (i.e., different combinations between pictures and sentences) for eachof the Active, Passive, and Scrambled sentence conditions, as well as for the
One-argument condition.
All stimuli were presented visually in yellow against a dark background (Fig.
1A). Each stimulus was presented for 5800 ms (intratrial interval) followed by a 200 ms
blank interval. To minimize the effect of general memory demands, a whole sentenceof a minimal length (i.e., two noun phrases and a verb) was visually presented for an
ample time for the patients to respond (see Table 2 for the reaction times). The stimuli
are thus more advantageous than sequentially presented stimuli that involve
memorization. For fixation, a red cross was also shown at the center of the screen to
initiate eye movements from the same fixed position, and the participants were
instructed to return their eyes to this position after the response. The stimulus
presentation and collection of behavioral data (error rates and reaction times) were
controlled using the LabVIEW software and interface (National Instruments, Austin,
Texas, USA). The participants wore earplugs and an eyeglass-like MRI-compatible
display (resolution, 800 × 600; VisuaStim XGA, Resonance Technology Inc.,
Northridge, California, USA).
Task
In the picture-sentence matching task, the participants read a sentence covertly andjudged whether or not the action depicted in a picture matched the meaning of the9sentence. They responded by pressing one of two buttons in a row (right for a matchedpair, and left for a mismatched pair). Under the Two-argument conditions, allmismatched sentences were made by exchanging two symbols in the original
sentences, e.g., “○ pulls ∆” instead of “∆ pulls ○”. Under the One-argument condition,
symbol-mismatched and action-mismatched sentences were presented equally often,
requiring the sentences to be read completely. The participants underwent shortpractice sessions before task sessions to become fully familiarized with this task.
Using the same stimulus sets of pictures and letters presented under both
Two-argument and One-argument conditions, we tested a Control task, in which theparticipants judged whether or not two head symbols in the picture matched those at
the bottom, irrespective of their order (Fig. 1A). The letters in hiragana were jumbled
without changing the head symbols and kanji, so that the letter string prevented evenbasic word recognition. General cognitive factors such as visual perception of thestimuli, matching, response selection, and motor responses were controlled by theControl task, and obviously by the One-argument condition as well.
A single run of the task sessions (306 s) contained 24 “test events” of thepicture-sentence matching task (six times each for the Active, Passive, and Scrambled
sentences, as well as for One-argument), with variable inter-trial intervals of one (6 s)or two (12 s) Control tasks. The order of the test events was pseudorandomized
without repetition of the same condition to prevent any condition-specific strategy. A
single run contained 27 trials of the Control task.
Eight runs were tested per one participant in a day. Considering the medical
conditions of patients, we limited the MR scanning time to one hour with inter-task
intervals of 5 min, restricting the number of in-scanner runs. All patients underwent
three or four in-scanner runs, and they performed out-scanner runs to make up eightruns. Normal participants were also tested under the same conditions, i.e., fourin-scanner and four out-scanner runs. We analyzed behavioral data for the eight runs.
MRI data acquisition
The functional MRI scans were conducted on a 1.5 T scanner (Stratis II, Premium;Hitachi Medical Corporation, Tokyo, Japan). We scanned 26 axial slices of 3-mm
thickness with a 1-mm gap, covering from –40 to 63 mm from the anterior to posterior
commissure line in the vertical direction, using an echo-planar imaging sequence(repetition time = 3 s, echo time = 50.5 ms, flip angle = 90°, field of view = 192 × 192
10

mm2, resolution = 3 × 3 mm2). In a single scanning run, we obtained 102 volumes
following three dummy images, which allowed for the rise of the MR signals. Aftercompletion of the functional MRI sessions, high-resolution T1-weighted images of thewhole brain (192 axial slices, 0.75 × 0.75 × 1 mm3) were acquired from all participants
with a radio frequency spoiled steady-state acquisition with a rewound gradient echo
sequence (repetition time = 30 ms, echo time = 8 ms, flip angle = 60°, field of view =192 × 192 mm2).
The diffusion tensor imaging scans were conducted on a 3.0 T scanner
equipped with an 8-channel phased-array head coil (Signa HDxt; GE Healthcare,
Milwaukee, Wisconsin, USA). We scanned 50 axial slices of 3-mm thickness without
gaps, covering from –60 to 90 mm from the anterior to posterior commissure line in thevertical direction, using a diffusion-weighted spin-echo echo-planar imaging sequence(b-value = 1000 s/mm2, repetition time = 15 s, echo time = 86.6 ms, field of view = 256
× 256 mm2, resolution = 2 × 2 mm2, number of excitations = 2). A single image without
diffusion-weighting (b0) was initially acquired, and then diffusion-weighting was
isotropically distributed along 60 diffusion-encoding gradient directions. Aftercompletion of the diffusion tensor imaging sessions, high-resolution T1-weighted
images of the whole brain (192 axial slices, 1 × 1 × 1 mm3) were acquired from allparticipants with a fast spoiled gradient recalled acquisition in the steady state
sequence (repetition time = 10.4 ms, echo time = 4.38 ms, flip angle = 25°, field of view= 256 × 256 mm2).
Functional MRI data analyses
Both group and single-subject analyses were performed in a standard manner using
SPM8 statistical parametric mapping software (Wellcome Trust Centre for
Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm/) (Friston et al., 1995), implemented on
MATLAB software (MathWorks, Natick, Massachusetts, USA). The acquisition timingof each slice was corrected using the middle slice (the thirteenth slice chronologically)
as a reference for the echo-planar imaging data. We realigned the time-series data to
the first volume in each run, and removed runs that included data with a translation of> 2 mm in any of the three directions and with a rotation of > 1.4° around any of thethree axes; these thresholds of head movement were empirically determined from ourprevious studies (Hashimoto and Sakai, 2002; Suzuki and Sakai, 2003; Kinno et al.,2008). For this reason, a single run was removed from three normal participants, two11

patients in the LPMC group, three patients in the F3 group, and two patients in the
Other group, which was about 10% of all time points.
Each participant’s T1-weighted structural image was coregistered to the meanfunctional image generated during realignment. The coregistered structural image
was spatially normalized to the standard brain space as defined by the Montreal
Neurological Institute using the “unified segmentation" algorithm with medium
regularization, which is a generative model that combines tissue segmentation(excluding “other” tissues like a lesion, etc.), bias correction, and spatial normalization(Ashburner and Friston, 2005). All of the normalized structural images were visuallyinspected and compared with the standard brain for the absence of any further
deformation. A previous study has suggested that the unified models provide betterand more reliable matching for brain images with focal lesions (Crinion et al., 2007).
After spatial normalization, the resultant deformation field was applied to the realigned
echo-planar imaging data in each run, which was resampled every 3 mm using
seventh-degree B-spline interpolation. All normalized functional images were thensmoothed by using an isotropic Gaussian kernel of 9 mm full-width at half maximum.
Low-frequency noise was removed by high-pass filtering at 1/128 Hz.
In a first-level analysis (i.e., fixed-effects analysis), each participant’s
hemodynamic responses induced by the trials were modeled with a boxcar function
with a duration of 6 s from the onset of each stimulus, and the boxcar function was
convolved with a hemodynamic response function. The functional data for trials withcorrect and incorrect responses in the picture-sentence matching task were separately
modeled. To minimize the effect of head movement, the six realignment parameters
obtained from preprocessing were included as a nuisance factor in a general linear
model. The images of the Active, Passive, and Scrambled sentence conditions, aswell as those of the Two-argument, One-argument, and Control, were then generated
in the general linear model for each participant, and used for intersubject comparisonsin a second-level analysis (i.e., random-effects analysis).
To discount any general effects associated with performance differencesamong the participants, individual error rates averaged among the Two-argument
conditions, or those of the One-argument condition for the One-argument – Control
contrast, were entered as a nuisance factor in a second-level analysis. Note that error
rates were more sensitive among the Two-argument conditions than reaction times(see Table 2). The results of paired t-tests (Two-argument – Control, One-argument –12

Control, or Two-argument – One-argument) were thresholded at P < 0.005 for thevoxel level, and at corrected P < 0.05 for the cluster level, with topological false
discovery rate correction across the whole brain (Chumbley and Friston, 2009).
An analysis of covariance with F-test was performed with two factors [group(LPMC, F3, Other) × condition (Active, Passive, Scrambled)] using the toolbox“Non-Stationary Cluster Extent Correction for SPM”
(http://fmri.wfubmc.edu/cms/software#NS), the results of which were thresholded at P
< 0.005 for the voxel level, and at corrected P < 0.05 for the cluster level, withfamily-wise error correction across the whole brain. Individual error rates were enteredas a nuisance factor for each of the Active, Passive, and Scrambled sentence
conditions. For the anatomical identification of activated regions, we basically used theAnatomical Automatic Labeling method (Tzourio-Mazoyer et al., 2002). For eachregion of interest, the mean percent signal changes were extracted from the local
maximum using the MarsBaR-toolbox (http://marsbar.sourceforge.net/).
Functional connectivity analyses
By using functional MRI data, functional connectivity among multiple regions wasassessed by a partial correlation method for the time-series data of the Normal group.
Using MarsBaR-toolbox, the time-series data were first averaged within a sphere of
6-mm radius centered at the local maximum of each region. To discount the global
differences of signal changes among the runs, the time-series were normalized foreach run. From each of the time-series of two regions in question, we regressed out all
the other nodes, before estimating the correlation between the two. For eachparticipant, partial correlation coefficients for each pair of regions were calculatedusing MATLAB, and they were averaged among all participants to create a partial
correlation matrix (Fig. 7C).
We tested whether the non-diagonal correlations within individual networks
were significantly greater than those between any of two networks. Because the partial
correlation coefficients were not normally distributed, we adopted the randomization
analyses (Nichols and Holmes, 2002); we randomized the labels of the regions,reordered their corresponding columns, computed partial correlation coefficients foreach pair of regions, and then averaged them among all participants. For eachrandomized partial correlation matrix, we averaged the non-diagonal correlations
within individual networks, as well as those between two networks, and subtracted the13latter from the former. Repeating these steps 10,000 times, we estimated the P-value
for the significance of an observed difference in partial correlations.
Diffusion tensor imaging data analyses
Data analyses of diffusion tensor imaging were performed using FSL (Oxford Centre
for FMRIB Software Library 4.1.7; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) and FDT(FMRIB’s Diffusion Toolbox 2.0) (Smith et al., 2004). Diffusion-weighted images werefirst resliced to isotropic voxels of 1 mm3, and then eddy current distortions and motion
artifacts were corrected using affine registration to the b0 image. We then extracted
the brain shape from the b0 image, and created the binary mask image (i.e., zero forthe outside of the brain) for each participant. Markov Chain Monte Carlo sampling was
performed to build up distributions on diffusion parameters at each voxel, whichallowed for estimation of the most probable pathway by Bayesian estimation (numberof fibers modeled per voxel = 2) (Behrens et al., 2007). The implicit modeling of noise
in a probabilistic model made it possible to track the fibers near the grey matter.
By using FLIRT (FMRIB’s Linear Image Registration Tool) on FSL, the b0
image was first coregistered to the individual T1-weighted image for each participant,and the T1-weighted image was spatially normalized to the Montreal Neurological
Institute space by using both affine and nonlinear transformations with FLIRT and
FNIRT (FMRIB’s Nonlinear Image Registration Tool). With the transformation matrices
and estimated deformation fields, the coordinates of each region were transformed
back to the individual b0 images, and a sphere of 6-mm radius centered at thetransformed coordinates was defined as a seed mask for the probabilistic tractography.
All fiber tracking was conducted in an individual diffusion tensor imaging space.
To find the connections between two regions of interest, we set two seedmasks and retained only those tracts that passed through both seed masks.
Probabilistic fiber tracking was initiated from all voxels within the seed masks togenerate 10,000 streamline samples, with a step length of 0.5 mm, a maximum
number of steps of 2,000, a curvature threshold of 0.2 (± 78.5°), and a loopcheck
option. For the pathway of the opercular/triangular parts of the right F3 & right posterior
superior/middle temporal gyri (“&” denotes a pair of seed masks), as well as that of theleft angular gyrus & lingual gyrus, an exclusion mask of the corpus callosum and fornix
was applied; for the pathway of the opercular/triangular parts of the right F3 &pre-supplementary motor area, an exclusion mask of the midbrain was applied.
14

In the connectivity distributions obtained, each voxel value represented thetotal number of the streamline samples passing through that voxel. The connectivity
probability maps were then created for each participant by dividing the connectivity
distributions with a sum of the waytotal values, i.e., the total number of generated
tracts from one seed mask that reached the other seed mask. This normalization
approach allowed for a comparison of the connectivity probability values acrossparticipants; note that the pattern of connectivity did not change by this scaling. To
remove any spurious connections, the pathways in individual participants were
thresholded to include only voxels that had at least 1% connectivity probability values(Flöel et al., 2009). The thresholded pathways in each participant were spatiallynormalized, and then binarized using “fslmaths” on FSL. The binarized pathways wereoverlaid across participants to produce a population probability map for each pathway,
in which the voxel values represent the number of participants with a pathway throughthat voxel. The population probability map with thresholding (at least five out of 11
participants) was smoothed and presented using MRIcroN software.

15

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