ORIGINAL RESEARCH
published: 14 June 2016
doi: 10.3389/fnhum.2016.00295

The Dorsal Rather than Ventral
Pathway Better Reflects Individual
Syntactic Abilities in Second
Language
Kayako Yamamoto 1,2 and Kuniyoshi L. Sakai 1,3 *1Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan, 2 Japan
Society for the Promotion of Science, Tokyo, Japan, 3 Core Research for Evolutional Science and Technology (CREST),Japan Science and Technology Agency, Tokyo, Japan

Edited by:
Tetsuo Kida,
National Institute for Physiological
Sciences, Japan
Reviewed by:Emma Sprooten,
Icahn School of Medicine at Mount
Sinai, USA
Corianne Rogalsky,
Arizona State University, USA*Correspondence:
Kuniyoshi L. Sakai
sakai@mind.c.u-tokyo.ac.jp
Received: 10 February 2016
Accepted: 01 June 2016
Published: 14 June 2016
Citation:
Yamamoto K and Sakai KL (2016)
The Dorsal Rather than Ventral
Pathway Better Reflects Individual
Syntactic Abilities in Second
Language.
Front. Hum. Neurosci. 10:295.
doi: 10.3389/fnhum.2016.00295

The left inferior frontal gyrus (IFG) has been reported to be critically involved insyntactic processing, not only in first language (L1), but in second language (L2).
Indeed, the leftward lateralization of the IFG has been shown to be correlated withthe performance of a syntactic task in L2. Given that posterior language-related
regions are systematically connected with the left IFG, the next question is which of
the dorsal and ventral pathways is more critical to the individual syntactic abilities in
L2. Here we used diffusion magnetic resonance imaging (MRI) and tractography with
newly developed semi-automatic methods of defining seeds and selecting regionsof interest (ROIs). We calculated mean thickness and fractional anisotropy (FA) ineach ROI for the arcuate fasciculus (Arcuate) of the dorsal pathway, as well as forthe inferior fronto-occipital fasciculus (IFOF) of the ventral pathway. In ExperimentI, we performed partial correlation analyses between FA and the accuracy of thesyntactic task, removing the effects of the accuracy of a spelling task, gender, andhandedness. Among the two pathways in each hemisphere, only FA of the left
Arcuate was significantly correlated with individual accuracy of the syntactic task. In
Experiment II, we recruited monozygotic twins and examined to what extent their
L2 abilities and their structural properties were similar. Within twin pairs, the highest
significant correlation was observed for reaction times of the spelling task, while thecorrelation for the accuracy of the syntactic task was marginal; these two correlation
coefficients were significantly different. Moreover, the thickness of the left Arcuate
was highly correlated within pairs, while its FA, as well as the thickness/FA in theventral pathways, was not significantly correlated. The correlation coefficient for the
thickness of the left Arcuate was significantly larger than that of the left IFOF. These
results suggest that the thickness of the left Arcuate is more associated with theshared genetic/environmental factors, whereas both of mutually correlated FA in theleft Arcuate and individual syntactic abilities in L2 may be less prone to these sharedfactors.
Keywords: diffusion MRI, white matter, dorsal and ventral pathways, language acquisition, syntax, monozygotic
twins

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INTRODUCTION

In the present diffusion MRI study with probabilistic
tracking, we developed the following semi-automatic methods,
and minimized the manual procedures. Seeds were definedin a semi-automatic and consistent manner, and the tracts
were successfully reconstructed in both hemispheres for every
participant. We then selected a ROI in each tract at the
portion with the most uniform thickness, thereby excluding
the branching or curved portions. The ROI size for eachpathway was also optimized to minimize the individual
variances of thickness. By focusing on these ROIs, we wereable to reliably examine whether individual variances of FA,
independent of thickness, in the language-related pathways
reflect the individual linguistic abilities. In the present study,
we focused on individual differences in L2 acquisition, because
varieties in L2 abilities are usually larger than those in first
language (L1).
Here we recruited Japanese students who had learned English
as L2, and assessed their individual syntactic abilities using a
syntactic error-detection (Syn) task in English. The syntactic
errors were basically related to the argument structures ofEnglish verbs. In some linguistic theories, it is assumed that‘‘lexical entries contain at least some syntactic information, in
addition to the phonological and semantic information thatsurely must be present’’ (Chomsky, 1995). Previous functional
MRI studies have employed tasks for argument structures,
revealing the selective activations for syntactic decisions in
the left inferior frontal gyrus (IFG; Suzuki and Sakai, 2003;
Raettig et al., 2010). Magnetoencephalography (MEG) studies
have also shown selective responses in the left IFG to verbs
depending on argument structures, thereby controlling theeffects of lexico-semantic factors (Iijima et al., 2009; Inubushi
et al., 2012; Iijima and Sakai, 2014). Cortical regions other thanthe left IFG may also contribute to syntactic processing. It has
been reported that damage to the left temporo-parietal cortex
including the middle/superior temporal gyri and the angulargyrus is related to the decreased performances of sentencepicture matching tasks with several sentence types (e.g., active,
passive, and cleft sentences; Dronkers et al., 2004; Thothathiri
et al., 2012; Magnusdottir et al., 2013). However, words usedin these tasks may also have pragmatic or semantic effects,
and the functions of these regions may be explained by these
additional effects. Returning to the functions of the left IFG insyntactic processing, we have clarified its critical involvement not
only for L1, but for L2. In our previous functional MRI study(Sakai et al., 2009), we used the same tasks as in the present
study, including a spelling error-detection (Spe) task to control
semantic and related cognitive factors. We have demonstrated
that the individual accuracy of the Syn task was positively
correlated with activations of the dorsal triangular part (F3t) of
the left IFG. Moreover, our voxel-based morphometry (VBM)
study with the same tasks showed that the accuracy of the Syntask, but not of the Spe task, was correlated with the leftward
lateralization of a single region’s volume in the F3t (Nauchi
and Sakai, 2009). These functional MRI and VBM studies haveprovided a consistent view that the individual differences bothin the function and anatomy of the left IFG were related to theindividual syntactic abilities. Regarding connectivity including

It has been widely accepted that the cortical regions supporting
language functions are mainly connected by dorsal and ventral
pathways (Hickok and Poeppel, 2007; Friederici, 2011). The
recent development of diffusion magnetic resonance imaging(MRI) techniques has opened an avenue for the structural studies
of fiber bundles in a non-invasive manner, by enabling, forexample, the assessment of each bundle’s fractional anisotropy(FA)—the degree of fiber organization and/or myelination. The
bundle’s volume or thickness is another structural property
independent of FA, since it is possible that the tract becomes
thicker even when the tract has lower FA. FA has been reported
to increase in white matter underlying the intraparietal sulcus
after a 6-week training of a visuo-motor skill (Scholz et al.,2009), as well as in the genu of the corpus callosum during a
9-month training of a second language (L2; Schlegel et al., 2012).
These studies have induced researchers to examine a correlation
between FA and performance metrics. In order to reveal thefunctional roles of the language-related pathways, it would be
important to separate syntactic or semantic abilities from othergeneral skills, and to further identify which structural propertiesof dorsal and/or ventral pathways actually reflect individual
differences in these abilities.
When tracking pathways in an individual brain with diffusion
MRI, we should assess the structural properties of a certain
tract with caution, because in some studies not all of the rightarcuate fasciculus (Arcuate), which overlaps with the superior
longitudinal fasciculus (SLF), were reconstructed (Catani et al.,2007; Lebel and Beaulieu, 2009; Thiebaut de Schotten et al., 2011;
López-Barroso et al., 2013). Possible causes of such uncertainty
may be the limitations of deterministic tracking algorithms(Yeatman et al., 2011), and/or other methods for tracking
bundles—for example, starting points (i.e., seeds) were drawn
manually in many studies, making the strict inclusion/exclusionof certain fibers difficult. The use of probabilistic tracking
algorithms, as well as a reliable and objective method for seeddefinition, is thus necessary. Another difficulty in examining
structural properties of individual brains is that macroscopic
differences may depend on a number of structural factors, suchas the degrees of arching and branching, as well as individual
variations in the shape and size of tracts, especially near thegray matter (Catani et al., 2005; Anwander et al., 2007). One
approach for focusing on areas of small inter-subject variability
is to select regions of interest (ROIs) that exclude potentiallyvariable branching regions (Tsang et al., 2009; Yeatman et al.,2011). Although there is another strategy of Tract-Based Spatial
Statistics (TBSS), in which voxels in the center of fiber bundles
with the highest FA (i.e., the ‘‘mean FA skeleton’’) are examined(Smith et al., 2006), it has been argued that the mean FA skeleton
is a very limited portion at the center (Van Hecke et al., 2010;
Bach et al., 2014). Selecting ROIs has an advantage over TBSS,
such that the thickness (or volume), i.e., another structuralproperty, can be obtained at a full-width cross-section of a fiber
bundle. Here we employed this ROI approach, and tried to
improve the method of determining the size and position of ROIs
objectively.

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correlations within monozygotic twin pairs for each of thestructural and behavioral parameters to explore the extent of
their dependence on shared factors. We assessed the behavioral
correlations within monozygotic twin pairs (Experiment II),
thereby examining the extent to which the accuracy andreaction times (RTs) in the Syn/Spe tasks in L2, as well asa verbal fluency in L1, are associated with shared factors;
we do not intend to dissociate genetic and environmental
factors here. We also examined whether the white matter
properties, i.e., the thickness and FA, were associated withshared factors. Our present study with monozygotic twins,
together with appropriate methods to examine the structural
basis of syntactic abilities, should shed new light on theneural framework underlying individual differences in L2
acquisition.
Here we conducted diffusion MRI acquisition and fiber
tracking by using q-ball imaging. The q-ball imaging technique,a q-space analysis of diffusion MRI with a relatively high b-value(e.g., 4000 s/mm2 ), was developed for improved reconstructionof the fiber tracts (Tuch, 2004). On the other hand, the b-value
was often lowered to 1000 s/mm2 for a better estimation of
FA (Jones and Basser, 2004). For high b-values, the assumptionthat the diffusion signal decay is monoexponential may not besuitable (Clark and Le Bihan, 2000), but such a difference in FA issmaller with a 3 T scanner than with a 1.5 T scanner (Chung et al.,2013). In the present study we used a 3 T scanner and conducteda pilot experiment to examine the effects of imaging parameters.

regions adjacent to the left IFG, resting-state connectivity
between the left anterior insula/frontal operculum and the left
superior temporal region has been reported to show a positive
correlation with other L2 abilities including lexical retrieval (Chaiet al., 2016). In such resting-state connectivity, however, thefunctional roles of those pathways remain unknown. It would be
important to examine the correlation between behavioral metrics
and any properties of anatomically or functionally identified
pathways. Given our previous studies suggesting the criticalinvolvement of the left IFG to syntactic processing in L2, a nextquestion is to clarify which of structural properties of whitematter fibers connecting the left IFG reflect individual syntactic
abilities.
Recent diffusion MRI studies have anatomically identified
several dorsal and ventral pathways connecting inferior frontal
regions with other cortical regions supporting language functions(Catani et al., 2012). Our recent study, using functional/diffusion
MRI as well as functional connectivity analyses, showed thatthe left dorsal pathway including the Arcuate/SLF transmitted
information from the left IFG to the supramarginal gyrus, wheresyntax-selective activations were observed (Ohta et al., 2013).
On the other hand, a lesion study reported that disconnectionin either the dorsal or ventral pathway was associated withincreased errors in a sentence-picture matching task, suggestingthat both the dorsal and ventral pathways are critical for syntax(Griffiths et al., 2013). In order to reveal the roles of connectivity
including the left IFG in L2 acquisition, it is thus important toseparate syntactic abilities from other linguistic or general ones,
and to further clarify whether the structural properties of thedorsal and/or the ventral pathways actually reflect individual
syntactic abilities. In the present study, we focus on the Arcuateof the dorsal pathway, as well as the inferior fronto-occipitalfasciculus (IFOF) of the ventral pathway, and hypothesize thatstructural properties of the left Arcuate reflect the individual
syntactic abilities in L2. We recruited native Japanese speakers,
who had studied English as L2 for about 5 years, and assessed
their syntactic abilities in L2 based on the accuracy of the Syntask (Experiment I).
We further examined to what extent L2 abilities and the brain
structures are associated with factors that are shared amongindividuals (e.g., a particular educational environment), and withthose that are not shared among individuals (e.g., individual
aptitudes for multiple languages). Because monozygotic twins
share genetic and some environmental factors, twin studies
can be used to explore the extent to which brain structure
or function is influenced by shared genetic and environmental
determinants. A monozygotic twin study has reported thatthe volume of each of four lobes was strongly affected byshared genetic/environmental factors, while the sulcal/gyral
curvature and the surface-to-volume ratio were more prone
to be affected by non-shared factors (White et al., 2002).
Monozygotic twins have also been studied to reveal the influenceof shared factors on sulcal patterning using a graph matching
method (Im et al., 2011). A possible direction of research
includes the following two steps: (i) identifying structuralproperties and behavioral performances that reflect specified
functions (as in our Experiment I); and (ii) examining the

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MATERIALS AND METHODS
Experiment I
Participants
We recruited senior high-school students in their fifth academic
year at the Secondary Education School attached to the Facultyof Education of the University of Tokyo, to which twinsare preferentially admitted for educational research. We set
the following basic inclusion criteria: (i) right-handedness asassessed by the Edinburgh inventory (Oldfield, 1971); (ii) no
history of neurological or psychiatric diseases; and (iii) native
Japanese speakers whose age of first exposure (AOE) to English
in formal education was 12 or 13 years old (a condition met
by the majority of students in this school). As regards thefirst criterion, participants with a negative laterality quotient(LQ) or those with a potential history of change in handedness
were dropped, resulting in a relatively strong right-handed
population (LQ > 50 in most cases). In Experiment I, foreach twin pair who met these criteria, the one exhibiting thehigher score for the Spe task was entered into the analysis
to avoid a double count of twins with potentially similar
characteristics. There were 47 participants who met these
conditions.
We set two additional criteria in Experiment I: (iv) longer RTs
for Syn than for Spe; and (v) performance accuracy in the upper
half of participants for Spe (80% or higher). The fourth criterion
was necessary because shorter RTs for Syn indicated the possible
abandonment of the more demanding task; two participants

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the sets of sentences were basically the same in the Syn andSpe tasks, and multiple words were naturally related to one
another to understand the meanings of these sentences inboth tasks.
All behavioral data were acquired outside the MR scanner.
At the initiation of every trial of 7 s, a cue, indicating
whether the task was Syn or Spe, was shown for 400 ms.
Next, a set of sentence stimuli was shown for 6400 ms. The
participants were instructed to read the sentences silently, andto indicate whether or not the sentences contained an error
by pushing one of two buttons. For each task, five trials wereconsecutively performed as a task block for better concentration
on each type of task, and these task blocks were alternately
performed. The initial order of task blocks was counterbalanced
across participants, thereby eliminating any effects of the orderof presentation [i.e., Syn—Spe—Syn. . . or Spe—Syn—Spe. . .].
Participants performed 50 trials, and after a short break, they
performed another 50 trials, accomplishing 100 trials in total (50each for Syn and Spe; 50 each for correct and incorrect stimuli,
completely randomized). As in the previous studies with thesame tasks, trials with no response within the allowed time were
included as incorrect ones in calculating the accuracy of eachtask; a response within 1.5 s was regarded as the response to
the trial just one before. As regards RTs, only trials with correct
responses were included. After the task instruction and brief
explanation, the participants received 10 training trials (five forSyn, five for Spe). Additional 10 trials were provided for someparticipants who needed to adapt to the time constraint of thetasks.
To test general skills in word processing, a verbal fluency
task in L1 (Japanese) was performed. For a given single letter inhiragana, participants were asked to write as many words (except
proper nouns) starting from this letter as possible within 60 s. The
verbal fluency was separately tested in three trials with different
letters, which consisted of an easier letter (sa) and two harder
letters (ri and nu).

TABLE 1 | Demographic details of the participants in each experiment.

Experiment I
Experiment II

N

Age

AOE

DOE

LQ

26
(15 females)24(18 females)

17 ± 0.3(16−17)17 ± 0.3(16−17)

12.6 ± 0.3(12−13)12.5 ± 0.3(12−13)4.5 ± 0.2
(4.3−4.8)
4.5 ± 0.2
(4.3−4.8)81 ± 18(38−100)
87 ± 13(63−100)

The number of participants (N), age, age of first exposure (AOE) to English,
duration of exposure (DOE) to English, and laterality quotient (LQ) are shown as
the mean ± SD. Ranges are shown in brackets.

were dropped for this reason. Given that the participants inthe present study did not receive any training sessions, it wasnecessary to set the fifth criterion in order to match the task
accuracy of Syn [both mean and standard deviation (SD)] withthat after the training reported in our previous studies (Nauchi
and Sakai, 2009; Sakai et al., 2009); 19 participants were dropped
for this reason. When the fifth criterion was not applied, theaccuracy of Syn for 20 out of the 47 participants was lower
than 55% (i.e., near the level of chance); such a population
was not appropriate for examining individual syntactic abilities.
Twenty-six participants met the five criteria, and demographic
details of these participants are shown in Table 1: age, AOE toEnglish, duration of exposure (DOE) to English, and LQ. Written
informed consent was obtained from all participants as well asfrom their guardians. The study was approved by the Secondary
Education School and by the Institutional review board of the
University of Tokyo, Komaba.

Stimuli and Tasks
We visually presented stimuli, which were English sentences
identical to those used after training sessions in our previous
studies (Nauchi and Sakai, 2009; Sakai et al., 2009). In brief,
we selected 42 high-frequency English verbs (20 transitive and
22 intransitive, including 12 unergatives and 10 unaccusatives(Yusa, 2003)), and used them to make sentence stimuli withother high-frequency words; each verb was used 1–3 times totest different properties of these verbs. Each stimulus consistedof a key sentence and its associated sentence, the latter ofwhich was either syntactically normal or anomalous in the Syntask. It is generally hard for L2 learners to acquire correct
argument structures and their associated syntactic knowledge.
For instance, Japanese students learning English as L2 tend toomit an object and to accept such incorrect sentences (∗ ) as‘‘Do you often meet Mary?—∗ Yes, I often meet,’’ since objects oftransitive verbs can be omitted quite freely in many languages
including Japanese. In this way, our task properly tested thestructural properties of verbs. The stimuli contained several
other types of errors, such as an incorrect use of intransitive
verbs (‘‘You can kill many monsters in the game.—∗ You will die
them in the game.’’) or subject drop (‘‘John often comes to myhouse.—∗ Soon will come.’’), requiring the relational propertiesof the constituents. Because these ungrammatical sentences
cannot be judged as incorrect by semantic information alone,
the accuracy of the Syn task appropriately reflected individual
syntactic abilities. In the Spe task, a typographical error was
included to test the English orthography of words. Moreover,

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MR Image Acquisition
The scans were conducted on a 3.0 T scanner (Signa HDxt;
GE Healthcare, Milwaukee, WI, USA) with an 8-channel phasedarray head coil. We set the axial sections of the brain inparallel with its anterior to posterior commissure (AC-PC) line.
We scanned 50 axial slices that were 3-mm thick without agap, covering the whole brain, with a diffusion-weighted spinecho echo-planar imaging sequence (b-value = 4000 s/mm2 ,repetition time (TR) = 17 s, echo time (TE) = 110 ms, fieldof view (FOV) = 192 × 192 mm2 , resolution = 3 × 3 mm2 ,number of excitations = 1). For this scanning, a single image
without diffusion-weighting (b0) was initially acquired, andthen diffusion-weighting was isotropically distributed along 60
diffusion-encoding gradient directions. After completion of the
diffusion MRI session, high-resolution T1-weighted images of
the whole brain (192 axial slices, 1 × 1 × 1 mm3 ) wereacquired from all participants with a fast spoiled gradient recalled
acquisition in the steady state sequence (TR = 10 ms, TE = 4 ms,
FA = 25◦ , FOV = 256 × 256 mm2 ).

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Diffusion MRI Data Analyses
We used q-ball imaging (Tuch, 2004), a q-space analysis ofdiffusion MRI, and performed data analyses using FSL [Oxford
Centre for Functional MRI of the Brain’s (FMRIB) Software
Library 4.1.91 ] and FMRIB’s Diffusion Toolbox (FDT) 2.0 (Smithet al., 2004). Diffusion-weighted images were first resliced to
an isotropic voxel size 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 shapefrom the b0 image, and created a binary mask image (zerofor the outside of the brain) for each participant to calculate
the diffusion tensor, three eigenvectors, and FA in each voxel
inside the brain. Fiber orientations were estimated for theconstant solid angle orientation distribution functions (ODFs)
using the spherical harmonic decomposition and residual
bootstrapping algorithm included in the QBOOT (q-ball ODFs
and residual bootstrap) toolbox in FSL (Sotiropoulos et al.,2013).
We performed tractography by a single-seed approachin the native space using the probtrackx tool of FDT; webasically followed the procedures described previously (Ohtaet al., 2013). Probabilistic fiber tracking was initiated fromeach voxel within a seed mask to generate 5000 streamline
samples in any direction, with a step length of 0.5 mm, a
maximum number of steps of 2000, a curvature thresholdof 0.2 (±78.5◦ ), and a loopcheck option. Each voxel value
represented the total number of streamline samples passing
through that voxel, making a connectivity distribution. The
connectivity probability maps were then created by dividing
the connectivity distribution with a waytotal value, i.e., thetotal number of generated streamline samples from the seedmask that had reached two waypoint masks. To remove
any spurious connections, tracts were thresholded to include
only voxels that had connectivity probability values of atleast 0.2%. Thresholded tracts were then binarized using thefslmaths function of FSL. In order to prevent the misplacementof seed and waypoint masks, all tracts were inspected toconfirm that they were not touching the peripheral edgesof the masks.

(orange ellipses in Figure 1A). On a participant’s color-coded
map of fiber bundles, we confirmed that the center-of-gravityof the transformed bottleneck mask was included in the tract’s
cross-section; the transformed point was manually adjusted tothe center of the tract within a few millimeters [mean ± SD;
Experiment: 3.8 ± 1.3 mm (left), 4.4 ± 2.1 mm (right);
Experiment II: 3.8 ± 1.6 mm (left), 4.0 ± 1.1 mm (right)].
The amount of adjustments had no hemispheric difference
according to the paired t-tests in Experiment I (t (25) = 1.3,
P > 0.1) or in Experiment II (t (23) = 0.4, P > 0.6). Ona coronal slice, we generated a seed mask of 25-mm square(the red square in Figure 1B), whose center was placed at thetransformed point. The resultant seed mask included the entire
cross-section of the Arcuate. These semi-automatic procedures
allowed us to define seed masks in a consistent manner among
participants.
To reliably extract the Arcuate alone, we used two waypoint
masks of 30-mm squares (blue squares in Figure 1B) that werelarge enough to include the tract’s cross-sections. A coronalmask was placed 20-mm anterior to the seed mask in the native
space, and an axial mask was placed 20-mm inferior to theseed mask’s center. For the coronal mask, its lowest edge wasparallel to that of the seed mask at the same z-coordinates; bothmasks’ centers also had the same x-coordinates. The axial mask’s
center had the same x-coordinates as the seed mask, and they-coordinate of the axial mask’s center was initially set 10 mm
posterior to the seed mask. By considering individual variability
near the arching part of the pathways, we adjusted the ycoordinate within a few millimeters [Experiment I: 2.1 ± 2.7 mm(left), –1.3 ± 3.7 mm (right); Experiment II: 2.8 ± 2.5 mm(left), –1.2 ± 2.7 mm (right)]. The amount of adjustments hadhemispheric differences in Experiment I (t (25) = 5.1, P < 0.05)
and in Experiment II (t (23) = 7.3, P < 0.05), indicating thatthe curvature of the Arcuate was different in each hemisphere.
We also used a set of exclusion masks in three planes toavoid the potential inclusion of fibers other than the Arcuate:a coronal mask (15-mm square) to exclude the IFOF, an axial
mask (30-mm square) to exclude the corona-radiata, and a
mid-sagittal plane to exclude commissural fibers. The coronal
exclusion mask was the same as the seed mask for the IFOF
(see below), and the axial exclusion mask was placed 10 mm
superior to the center of the coronal exclusion mask, andindividually adjusted in the axial plane to cover a narrower
portion of the central vertical fibers on the color-coded map offiber bundles.
As regards the IFOF, we again placed a bottleneck mask(a sphere of 10-mm radius) at a region (±35, 0, −5) in
the MNI space where a narrower portion of the IFOF was
expected to pass, with reference to the atlases of white matter
pathways. The bottleneck mask was transformed back to thenative space of each participant (blue ellipses in Figure 1C).
We confirmed that the center-of-gravity of the transformed
bottleneck mask was included in the tract’s cross-section; thetransformed point was manually adjusted to the center of the
tract within a few millimeters [Experiment I: 4.9 ± 1.3 mm(left), 5.1 ± 2.0 mm (right); Experiment II: 4.5 ± 1.1 mm(left), 4.7 ± 2.0 mm (right)]. The amount of adjustments

Identification of the Arcuate and IFOF
By using FMRIB’s Linear Image Registration Tool (FLIRT) on
FSL, the b0 image was first coregistered to the individual T1weighted image for each participant, and the T1-weighted image
was spatially normalized to the Montreal Neurological Institute(MNI) space by using both affine and nonlinear transformation
with FLIRT and FMRIB’s Nonlinear Image Registration Tool
(FNIRT). To reliably set the center of a seed mask, we placeda ‘‘bottleneck mask’’ (a sphere of 10-mm radius) at a region(±35, −35, 30) in the MNI space where a narrower portionof the Arcuate was expected to pass, with reference to theatlases of white matter pathways (Catani and Thiebaut de
Schotten, 2008; Oishi et al., 2011). With the transformation
matrices and estimated deformation fields, the bottleneck mask
was transformed back to the native space of each participant
1 http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/

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FIGURE 1 | The Arcuate and inferior fronto-occipital fasciculus (IFOF) reconstructed by diffusion magnetic resonance imaging (MRI) in a typicalparticipant. (A) A color-coded map of fiber bundles in the native space of a participant. In the Montreal Neurological Institute (MNI) space, we placed a “bottleneckmask” (a sphere of 10-mm radius) at a region where a narrower portion of the pathway was expected to pass. The mask was then transformed back to the native
space. The orange ellipses denote the approximate outline of the transformed bottleneck mask on the left Arcuate. (B) The reconstructed left Arcuate. On a coronalslice, we generated a seed mask (shown in red), whose center was placed at the center of the transformed bottleneck mask on the left Arcuate. The waypoint masks
are shown in blue. (C) A color-coded map of fiber bundles for the same participant. The blue ellipses denote the outline of the transformed bottleneck mask on theleft IFOF. (D) The reconstructed left IFOF. On a coronal slice, we generated a seed mask (shown in red), whose center was placed at the center of the transformed
bottleneck mask on the left IFOF. A, anterior; P, posterior; S, superior; I, inferior; L, left; R, right; AC, anterior commissure; PC, posterior commissure.

had no hemispheric difference according to the paired t-tests
in Experiment I (t (25) = 0.5, P > 0.5) or in Experiment II(t (23) = 0.3, P > 0.6). On a coronal slice, we generated aseed mask of 15-mm square (the red square in Figure 1D),
whose center was placed at the transformed point. To include
longer tracts connecting both the IFG and occipital regions, weused waypoint masks at two coronal planes in each individual
hemisphere: one at the head of the caudate nucleus, and theother at the parieto-occipital sulcus (blue squares in Figure 1D;
Forkel et al., 2014). We also used a set of exclusion masks
in two planes to avoid the potential inclusion of fibers otherthan the IFOF: a coronal mask to exclude the Arcuate andsuperior fronto-occipital fasciculus (Forkel et al., 2014), and a
mid-sagittal mask to exclude commissural fibers. The coronal

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exclusion mask covered only the upper half of the native MR
image, and was placed at the anteroposterior midpoint of theimage.

ROI Selection to Minimize Individual Variances in the
Fiber Tract’s Thickness
The thickness of the fiber tract was defined as the numberof voxels (voxel size, 1 mm3 ) at a certain section of the
tracked fibers. Here we chose coronal sections, because theportions without branching in the Arcuate or IFOF were nearlyhorizontal. We measured the thickness along the Arcuate in eachhemisphere for the length of 35 mm, i.e., cross-sections L1 ∼L35of the upper half of the image, corresponding to the 94th∼128th
coronal slices from the anterior end of the image (see Figure 2A).

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FIGURE 2 | Region of interest (ROI) selection to minimize individual variances in the fiber tract’s thickness. (A) A ROI (a segment shown by blue lines)
in the left Arcuate reconstructed for the participant shown in Figure 1. Thickness was defined as the number of voxels (voxel size, 1 mm2 ) at each coronal section of
the tracked fibers. In the lower graph, the tract’s thickness is shown at each of the L1 ∼L35 cross-sections for the upper half of the brain (above the horizontal dashedline). Within a tract segment of a fixed length (e.g., 20 mm), we calculated the standard deviation (SD) of the tract’s thickness, and slid the segment between L1 andL35 . Among these segments, the segment with a minimal SD, i.e., with the most uniform thickness, was selected as a ROI. (B,C) The averaged thickness and SD forvarious ROI sizes in the Arcuate. The mean thickness in the ROI was further averaged, and the SD was obtained among the participants. The averaged thickness or
SD in the left and right hemispheres is shown by the solid and dashed lines, respectively. To minimize the SD, we chose a ROI size in the range of 19–21 mm(blue-shaded), in proportion to the individual size of the brain. (D) A ROI (a segment shown by orange lines) in the left IFOF reconstructed for the same participant. In
the lower graph, the tract’s thickness is shown at each of the L1 ∼L70 cross-sections. Among these segments, the segment with a minimal SD was selected as a
ROI. (E,F) The averaged thickness and SD for various ROI sizes in the IFOF. The averaged thickness or SD in the left and right hemispheres is shown by the solid anddashed lines, respectively. We chose a ROI size in the range of 14–16 mm (blue-shaded), in proportion to the individual size of the brain.

Within a tract segment of a fixed length (e.g., 20 mm), we
calculated the SD of the tract’s thickness, and slid the segment
between L1 and L35 . Among these segments, the segment with a

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minimal SD, i.e., with the most uniform thickness, was selected
as a ROI. To objectively determine its appropriate length or ROI
size, we varied it from 10 to 30 mm, and the ‘‘mean thickness’’ was7

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calculated among the selected cross-sections. The mean thickness
was further averaged among the participants (Figures 2B,C).
The averaged thickness started to increase when the ROI sizes
exceeded 20 mm in both hemispheres (Figure 2B), indicating thecontribution from branching portions. In the left hemisphere, theSD of the mean thickness among participants was lowest for the
ROI size of around 20 mm (Figure 2C). We thus chose a ROI
size in the range of 19–21 mm, in proportion to the individual
size of the brain. For 47 out of 52 tracts in all hemispheres tested,
the final ROIs did not reach the end of L1 or L35 ; thus the searchvolume of L1 ∼L35 was large enough to determine most ROIs.
As regards the IFOF, we measured thickness for the lengthof 70 mm, i.e., cross-sections L1 ∼L70 , corresponding to the84th∼153th coronal slices from the anterior end of the image(see Figure 2D). Within a tract segment of a fixed length (e.g.,
15 mm), we calculated the SD of the tract’s thickness, and slid
the segment between L1 and L70 . Among these segments, thesegment with a minimal SD was selected as a ROI. To objectively
determine an appropriate length or ROI size, we varied itfrom 10 to 30 mm, and calculated the ‘‘mean thickness.’’ Theaveraged thickness among the participants started to increase
when the ROI sizes exceeded 15 mm in the left hemisphere(Figure 2E). In both hemispheres, the SD of the mean thickness
among participants was lowest for the ROI size of around
15 mm (Figure 2F). We thus chose a ROI size in the range of
14–16 mm, in proportion to the individual size of the brain.
For all tracts tested, the final ROIs did not reach the end of
L1 or L70 .

identified in each individual hemisphere, as described above.
We examined correlations within twin pairs separately forthe behavioral data and the structural (FA/thickness) data. To
examine the difference between correlations, we statisticallycompared correlation coefficients, using the cocor (comparing
correlations) package4 in R software (Diedenhofen and Musch,
2015).

Effects of Different Imaging Parameters
on FA
In a pilot experiment for 13 participants (including four pairsof monozygotic twins) from Experiment I/II, diffusion MRI
data were acquired with a b-value of 1000 s/mm2 (TR = 17 s,
TE = 90 ms, FOV = 256 × 256 mm2 , resolution = 2 × 2 mm2 ,number of excitations = 1), in addition to diffusion MR datawith a b-value of 4000 s/mm2 (see ‘‘MR Image Acquisition’’Section). We scanned 67 axial slices that were 2-mm thickwithout a gap, covering the whole brain. A single b0 image wasinitially acquired, and then diffusion-weighting was isotropically
distributed along 60 diffusion-encoding gradient directions, as
in Experiment I/II. In our MRI system, we were able to set asmaller voxel size for a lower b-value of 1000 s/mm2 (2 mm3 )than that for a b-value of 4000 s/mm2 (3 mm3 ). We confirmedthat FA from the imaging with a b-value of 4000 s/mm2 , which
has been used for improved reconstruction of the fiber tracts(see ‘‘Introduction’’ Section), was consistent with FA obtainedwith a lower b-value and higher resolution. By using FDT 3.0
and FLIRT on FSL 5.0.4, the individual b0 image with theb-value of 1000 s/mm2 was coregistered to that with the b-valueof 4000 s/mm2 . With the same transformation matrices, anindividual FA map with a b-value of 1000 s/mm2 was also
spatially transformed for each participant. We then compared
the FA values in the left Arcuate obtained with different b-values,
within the tract reconstructed by q-ball imaging with the b-valueof 4000 s/mm2 .

FA Analyses
As described in the above sections, ROIs were determined on
the basis of the connectivity probability and resultant thickness.
After selecting ROIs, voxels with FA values of 0.2 or higher
were selected, and this thresholding was used only in FA
analyses. The thresholding has been widely used in previous
studies to avoid the uncertainty of anisotropy measurementsin the peripheral regions of fibers. We calculated the mean
FA within the ROI, and examined the relationships between
the accuracy of the Syn task and FA in each pathway after
removing the effects of the accuracy of Spe, gender, and LQ, using
the ppcor (partial and semi-partial correlation) package2 in R
software3 .

RESULTS
Experiment I
Behavioral Data
Behavioral data of the Syn and Spe tasks are shown in Table 2.
For the verbal fluency task, we evaluated each individual’s
performance by adding together the number of words produced
for all trials (mean ± SD: 26.2 ± 6.3). The performance ofthe verbal fluency task was not correlated with any of theaccuracy/RTs in Syn or Spe (P > 0.2). The accuracy of Syn was
significantly lower than that of Spe (t (25) = 11.4, P < 0.0001),
and the RTs of Syn were significantly longer than those of Spe(t (25) = 12.6, P < 0.0001), indicating that the Syn task was moredemanding than the Spe task.
If a behavioral parameter reflected general cognitive factors,
such as task difficulty and reading proficiency, which werecommonly involved in the Syn and Spe tasks with somedifferences in degree, then that parameter should show a

Experiment II
In Experiment II, only monozygotic twin pairs (24 twins)
were included (Table 1); we set the first three inclusion
criteria (i)–(iii) used in Experiment I. Note that the criterion
for the accuracy of Spe was not employed in Experiment
II. Eight participants were analyzed in both Experiment I
and II, but these analyses were independent and did notconstitute selection and selective evaluation analyses. As in
Experiment I, the participants performed the Syn, Spe, andverbal fluency tasks, and MR imaging acquisition was conductedwith the same parameters. The Arcuate and IFOF were2 https://cran.r-project.org/web/packages/ppcor/
3 http://www.r-project.org/

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4 https://cran.r-project.org/web/packages/cocor/

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TABLE 2 | Behavioral data of the participants.
Accuracy (%)

Experiment I
Experiment II

RTs (ms)

Syn

Spe

Syn

Spe

61 ± 11(42−88)60 ± 8(44−73)86 ± 4.9(80−100)
82 ± 9.2(68−100)

4778 ± 383(3989−5378)
4806 ± 489(3989−5877)

4324 ± 410(3503−5006)
4316 ± 529(3503−5587)

Only trials with correct responses were included for the reaction times (RTs). Data
are shown as the mean ± SD. Ranges are shown in brackets.

correlation between Syn and Spe among the participants, when
considered their individual differences regarding these factors.
For example, a slower reader would show longer RTs for both
Syn and Spe. Therefore, as a contraposition, if another behavioral
parameter did not show such a correlation between Syn and Speamong the participants, that parameter reflects at least distinct
factors required in each task. We thus examined the relationships
between the performances of the two tasks. The accuracy of
Syn and that of Spe did not show a significant correlation(r = 0.20, P = 0.32; Figure 3A), suggesting that the accuracy
mainly reflected distinct abilities required in each task. On theother hand, the RTs of Syn and Spe showed a highly positive
correlation (r = 0.89, P < 0.0001; Figure 3B), indicating that theRTs reflected general cognitive factors common to both tasks.
We observed that the correlation coefficient between RTs was
significantly larger than that between the accuracy (Z = 3.1,
P < 0.05) by using the cocor (comparing correlations) package
in R software, confirming the different natures of the accuracy
and RTs. These results indicate that the accuracy of each task
for the participants reflected individual abilities required in eachtask.

Structural Properties of the Arcuate and IFOF
Structural properties obtained in each ROI of the Arcuate and
IFOF are shown in Table 3. Within the tested group, thedistribution was not significantly different from the normal
distribution for the mean FA and thickness in each ROIof the four pathways [the Shapiro and Wilk (1965) test;
P > 0.08]. As regards the mean FA, a two-way repeatedmeasures analysis of variance (rANOVA) [pathway (Arcuate,
IFOF) × hemisphere (left, right)] showed a significant main
effect of pathway (F (1,25) = 54.2, P < 0.001), but with neithera main effect of hemisphere (F (1,25) = 0.86, P = 0.36) nor
an interaction (F (1,25) = 0.73, P = 0.4). According to apaired t-test (two-tailed) in each hemisphere, the mean FA
in the ROI of the IFOF was significantly higher than that of
the Arcuate (left: t (25) = 5.3, P < 0.001; right: t (25) = 5.0,
P < 0.001). With respect to the mean thickness, an rANOVA
showed a significant main effect of pathway (F (1,25) = 9.0,
P < 0.01) and an interaction (F (1,25) = 26.9, P < 0.001),
but no main effect of hemisphere (F (1,25) = 1.2, P = 0.3).
Paired t-tests showed that the mean thickness in the ROIof the left Arcuate was significantly larger than that of theright Arcuate (t (25) = 3.7, P < 0.005), while that of theright IFOF was marginally larger than that of the left IFOF
(t (25) = 2.1, P = 0.049; significance level at α = 0.025,

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FIGURE 3 | Independence of the accuracy of the Syn and Spe tasks.
Individual behavioral parameters in Experiment I are plotted for comparing thetwo tasks. The dotted line indicates an inclusion criterion of Spe employed inExperiment I. The accuracy of Syn and Spe showed no significant correlation(A), while reaction times (RTs) of Syn and Spe were highly correlated (B). An
asterisk at r indicates corrected P < 0.05.

Bonferroni-corrected). These results indicated that the Arcuate
and IFOF were different in terms of both macroscopic andmicroscopic properties, i.e., thickness and FA, respectively,

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Dorsal Pathway and Syntactic Abilities(P > 0.5). These results demonstrate that FA of the left Arcuate
can be a selective indicator of individual performances for the
Syn task.

TABLE 3 | Structural properties of the Arcuate and IFOF.
Mean thickness (mm2 )Mean FA

Arcuate
IFOF

Left

Right

Left

Right

Difference

0.39 ± 0.02
0.44 ± 0.04

0.39 ± 0.02
0.43 ± 0.04

90 ± 1671 ± 1178 ± 2178 ± 13

P < 0.005
P = 0.049

Experiment II
Behavioral Correlations within Twin Pairs
Behavioral data for monozygotic twins are shown in Table 2.
Each monozygotic twin was randomly assigned as either
twin A or twin B in a pair. Within the twin A or twin B
group, the distribution of each of the task performancesin L2 was not significantly different from the normal
distribution (the Shapiro-Wilk test; P > 0.1). We examinedcorrelations within twin pairs for the behavioral data (twotailed, significance level at α = 0.0125, Bonferroni-corrected).
As regards the Syn task, the correlation of the accuracy
within pairs was marginally correlated (r = 0.66, P = 0.02;
Figure 5A), while the RTs were significantly correlated(r = 0.78, P = 0.003; Figure 5B). As regards the Spetask, the twins exhibited highly correlated performances interms of accuracy (r = 0.85, P = 0.0004; Figure 5C) andRTs (r = 0.96, P < 0.0001; Figure 5D). Additionally, thecorrelation coefficient for the RTs of Spe was significantly
larger than that for the accuracy of Syn (P < 0.05). With
respect to the verbal fluency task, which was also normally
distributed (P > 0.09), the total number of produced
words was significantly correlated for the twins (r = 0.81,
P = 0.001).
To further confirm that these correlations did notoccur by chance, each twin was randomly assigned to a
new pair to produce 10,000 permutations (Sakai et al.,2004). These random pairings resulted in a normal
distribution (RTs of Syn: mean r = −0.07, SD = 0.27;
accuracy of Spe: mean r = −0.08, SD = 0.29; RTs of
Spe: mean r = −0.08, SD = 0.26; verbal fluency: meanr = −0.08, SD = 0.29), establishing that the above results
were actually due to factors that each twin pair had incommon.

For each pathway of either hemisphere, the mean FA or mean thickness in each
ROI was obtained in Experiment I. Data are shown as the mean ± SD among theparticipants (N = 26). Hemispheric difference was assessed by paired t-tests.

even when the same objective methods for ROI selection were
employed.

FA of the Left Arcuate as an Indicator of Individual
Performances for the Syn Task
Within the tested group, the distribution was not significantly
different from the normal distribution for the behavioral
performances (the accuracy of Syn, and the number of produced
words in the verbal fluency task), as well as for the mean
FA in each ROI of the four pathways (the Shapiro-Wilk test;
P > 0.1). For each of the four pathways, we performed partial
correlation analyses between the standardized mean FA andthe standardized accuracy of Syn (two-tailed, significance levelat α = 0.0125, Bonferroni-corrected), removing the effects ofother major independent factors: the standardized accuracy ofSpe, gender, and LQ (there were no correlations among thesefactors: r < 0.3, P > 0.3). The mean FA of the left Arcuate
showed a significant positive correlation with the accuracy ofSyn (r = 0.54, P = 0.007; Figure 4A). In contrast, the mean
FA in the ROI of the right Arcuate was not significantly
correlated with the accuracy of Syn (r = 0.06, P = 0.8;
Figure 4B). As regards the IFOF, the mean FA in either
hemisphere was not significantly correlated with the accuracyof Syn (left: r = −0.34, P = 0.1; right: r = −0.09, P = 0.7;
Figures 4C,D). Additionally, considering that FA (≥0.2) in each
ROI might not be normally distributed, we tested all analyses
above for the median FA instead of the mean, and replicatedthe overall results (a partial correlation between the median
FA in the left Arcuate and the accuracy of Syn; r = 0.56,
P = 0.003). Regarding the accuracy of Spe, we performed
partial correlation analyses between the standardized mean FA
and the standardized accuracy of Spe, thereby removing theeffects of the standardized accuracy of Syn, gender, and LQ.
The mean FA of the left Arcuate did not show a significant
correlation with the accuracy of Spe (r = 0.04, P = 0.9).
None of the other pathways showed a significant correlation
with the accuracy of Spe (P > 0.19). Note that the rangeof the accuracy of Spe was limited due to the fifth inclusion
criterion (see ‘‘Participants’’ Section), and that the statisticalpower was lower. To further confirm the absence of the effectsof the load associated with word processing, we also performed
partial correlation analyses between the standardized mean FA
and the standardized performance of the verbal fluency task.
The mean FA of the left Arcuate was not correlated with theperformance of the verbal fluency task (r = 0.13, P = 0.5),
and the other pathways did not show significant correlations

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Structural Correlations within Twin Pairs
Within the twin A or twin B group, the distribution of
thickness/FA in the ROIs of the Arcuate and IFOF was not
significantly different from the normal distribution (the ShapiroWilk test; P ≥ 0.05). We analyzed the structural correlations
within twin pairs for the Arcuate (two-tailed, significance levelat α = 0.0125, Bonferroni-corrected). As regards the mean
thickness, the twins were highly correlated in the left hemisphere(r = 0.79, P = 0.002; Figure 6A), but not in the right hemisphere(r = 0.57, P = 0.05; Figure 6B). Random pairings for the
thickness of the left Arcuate resulted in a normal distribution(mean r = −0.05, SD = 0.29), confirming that the significant
correlation was due to factors that each twin pair had in common.
With respect to the mean FA, the correlation was not significant
in either hemisphere (left: r = 0.33, P = 0.30; right: r = 0.55,
P = 0.06; Figures 6C,D). For the IFOF, the correlation was not
significant in either hemisphere as regards the mean thickness(left: r = 0.19, P = 0.56; right: r = 0.05, P = 0.89) or the mean FA

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FIGURE 4 | Fractional anisotropy (FA) of the left Arcuate as an indicator of individual performances for the Syn task. The scatter plots of the standardized
accuracy of Syn and the standardized mean FA in each ROI are shown for the left Arcuate (A), right Arcuate (B), left IFOF (C), and right IFOF (D). The effects of thestandardized accuracy of Spe, gender, and laterality quotient (LQ) were removed. Uncorrected P values are also shown for each panel. Only the left Arcuate showeda significant correlation between FA and the accuracy of Syn.

were correlated (r = 0.56, P < 0.01). Because the assumptionof independence between the accuracy of the two tasks inExperiment I did not apply in Experiment II, we considered thatfurther correlation analyses among multiple metrics/ propertiesof behavior and structure (e.g., cross-correlation analyses(left: r = 0.57, P = 0.05; right: r = −0.21, P = 0.50). Indeed, thecorrelation coefficient for the thickness of the left Arcuate wassignificantly larger than that of the left IFOF (P < 0.05).
In Experiment II, where the criterion for the accuracy of
Spe was not employed, the accuracy of Syn and that of Spe

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FIGURE 5 | Behavioral correlations within twin pairs. (A–D) Correlations for accuracy/RTs of the Syn and Spe tasks are shown separately, and plotted for eachpair of monozygotic twins. Each twin was randomly assigned as either twin A or twin B in a pair. The twin pairs showed significant correlations for task performances.

for 1000 s/mm2 . To examine the global tendency, we calculated
the mean FA among the voxels in the entire range of L1 ∼L35
for each participant, and examined the correlation between themean FA values at different b-values. As shown in Figure 7B,
the mean FA with the higher b-value was exactly correlatedwith that having a lower b-value (r = 0.92, P < 0.0001).
These results confirm that the fiber tracts with a b-value of4000 s/mm2 faithfully replicated the properties of those with alower b-value.

between the accuracy of Syn in twin A and structural properties
in twin B, and vice versa) were inconclusive in Experiment II.

Effects of the Imaging Parameters on FA
In the pilot experiment, we examined whether the FA values
obtained with different b-values were correlated. As shownin Figure 7A, the overall profiles of FA averaged among theparticipants were similar for both b-values throughout the rangeof L1 ∼L35 in the left Arcuate, although FA itself was higher

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FIGURE 6 | The structural correlation of the left Arcuate within twin pairs. The mean thickness in the ROI of the left (A) or right (B) Arcuate is plotted for eachpair of monozygotic twins. The plot of the mean FA in the ROI of the left (C) or right (D) Arcuate is also shown. Note the significant correlation for the mean thicknessof the left Arcuate alone.

DISCUSSION

than in the right, while the IFOF was marginally thicker in theright hemisphere (Table 3). Secondly, we revealed that the mean
FA in the ROI of the left Arcuate was significantly correlatedwith the accuracy of the Syn task (Figure 4A). This positive
correlation was independent of gender, handedness (LQ), and theorthographic knowledge while reading sentences (the accuracyof the Spe task), as well as of age, AOE, and DOE, which werealready controlled among the participants (Table 1). Moreover,
FA in the left Arcuate was not significantly correlated with the

The main results are summarized as follows. First, we improved
the seed definition in a semi-automatic manner to reliably track
the Arcuate and IFOF (Figure 1), and successfully identified
those fiber tracts in both hemispheres of all participants. We thenobjectively selected their ROIs, thereby minimizing the variancesin thickness among participants (Figure 2), and further clarified
that the Arcuate was significantly thicker in the left hemisphere

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coefficient for the thickness of the left Arcuate was significantly
larger than that of the left IFOF. These results suggest that generaltask performances, as well as the thickness of the left Arcuate, aremore associated with the shared genetic/environmental factors,
whereas both of mutually correlated FA in the left Arcuate andindividual syntactic abilities in L2 may be less prone to theseshared factors.
There have been a few previous studies in which a ROI was
selected to exclude branching or curved portions of the Arcuate.
In a tractography study, a ROI of a 7-mm-long segment waschosen at a tightly bundled portion in each of the Arcuate and
SLF, with some overlaps between the resultant ROIs for thetwo bundles (Tsang et al., 2009); those ROIs were defined inreference to the individual central sulcus, and the mean FA
was obtained within each ROI. In another study, a ROI of a
10-mm-long segment was chosen in the Arcuate, where theaveraged FA among participants was highest in the entire tract,
instead of a curved region with lower FA (Yeatman et al., 2011).
As a result of these selection procedures, the selected ROIs
had similar positions in those studies, but some participants
might have had lower FA with the ROIs. Moreover, it was
not clear whether the ROIs were sufficiently large to represent
the tract itself. Because our ROIs with uniform thickness wereindependent of FA, the present results would be free from any
sampling bias of selecting particular regions with higher FA. In
the present study, the mean FA in the ROI of the IFOF was
significantly higher than that of the Arcuate in both hemispheres.
The ROIs of the IFOF did not include the thinnest portion thatwas previously found to have a lower and more unstable FA(Yeatman et al., 2012), probably due to other intermixed tissue;
such portions should be avoided in examining the relationshipbetween FA and behavioral measurements. While FA did notshow a hemispheric difference for the Arcuate or IFOF in thepresent study, we found that the left Arcuate was clearly thicker
than the right, a finding which may be related to the leftlateralized language functions. The IFOF, on the other hand,
was marginally thicker in the right hemisphere. This opposing
lateralization of the Arcuate and IFOF is consistent with aprevious study, where the laterality was assessed by the numberof streamlines in tractography (Thiebaut de Schotten et al., 2011).
The thickness in our ROIs was similar to the relative numberof streamlines of each pathway, while successfully excluding the
portions of branching or contamination with other tissue. Our
semi-automatic methods of seed definition and ROI selection
would be applicable to other pathways as well, and would thus
be of use for the investigation of hemispheric or individual
differences.
There were some limitations in the present methods. We
minimized subjective procedures in seed and waypoint selection,
but large individual variability hampered the totally automatic
selection, and therefore slight manual adjustments were stillnecessary. This might have affected the results especially of theright Arcuate, which is known to be highly variable amongindividuals. However, the degree of adjustment was small inboth hemispheres (mean, ≤5 mm) compared to the mask sizes(15–30 mm), and all tracts were inspected to confirm thatthey were not touching the peripheral edges of the masks (seeFIGURE 7 | The consistency of FA for different imaging parameters.
(A) At each of the L1 ∼L35 cross-sections along the left Arcuate (seeFigure 2A), FA with a b-value of 4000 s/mm2 (voxel size: 3 mm3 ) or
1000 s/mm2 (voxel size: 2 mm3 ) was further averaged among 13 participants(error bars, standard errors of the mean). (B) A correlation between FA for
different b-values. The mean FA among the voxels within L1 ∼L35 is plotted foreach participant.

performance of the verbal fluency task. Because the accuracy ofSyn was independent of that of Spe (Figure 3A), the accuracyof Syn mostly represented individual syntactic abilities in L2.
Thirdly, within monozygotic twin pairs, significant correlations
were observed in the RTs of Syn/Spe, the accuracy of Spe, and theverbal fluency in L1, while the accuracy of Syn was marginallycorrelated (Figure 5). Indeed, the correlation coefficient for theRTs of Spe was significantly larger than that for the accuracy ofSyn. Finally, the mean thickness in the ROI of the left Arcuate was
highly correlated within twin pairs (Figure 6A). The correlation

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a general proficiency scale for L2 (The Interagency Language
Roundtable scale; Linck et al., 2013). Future large-scale studies
using a variety of behavioral measures are necessary to reveal
which network is specifically involved in syntactic, semantic, andother processes supporting language functions.
With respect to the connectivity of the dorsal pathway, somestudies have indicated that the fronto-temporal connection of theleft Arcuate is further separable into two parallel pathways—one
terminating in the posterior superior temporal gyrus, and theother terminating in the middle temporal gyrus (Glasser andRilling, 2008)—while other studies have suggested that there arethree segments in the Arcuate/SLF: a long segment between thefrontal and the superior/middle temporal regions, a posterior
segment between the inferior parietal and temporal regions,and an anterior segment/SLF between the frontal and inferiorparietal regions (Catani et al., 2012). Moreover, the SLF may
be further divided into three branches (Thiebaut de Schotten
et al., 2012), whose correspondence to the anterior segment/SLF
remains unclear. In regard to the ventral pathway, it includes the
IFOF and uncinate fasciculus (UF) connecting the ventral frontal
regions. The IFOF connects the ventral IFG (BA 45/47) and someposterior regions in the parietal, posterior temporal, and occipitalcortex (Martino et al., 2010), whereas the UF connects theorbitofrontal cortex (BA 10/11/47) and anterior temporal lobe,
including the temporal pole (Catani et al., 2012). The ventral
pathway may also include partial connections, such as the EmC,
external capsule (EC), middle longitudinal fasciculus (MdLF),
and inferior longitudinal fasciculus (ILF; Makris and Pandya,
2009; de Champfleur et al., 2013), although their structuralcomplexity, as well as the available scanning resolution, hampers
the dissociation of these connections. There is thus need of amethod to clarify the detailed connectivity of these pathways.
One critical method would be to define seeds reliably andobjectively, thereby combining the results from various imaging
techniques, including diffusion MRI, functional MRI, and VBM.
The functional roles of the dorsal and ventral pathways havealso been a matter of debate. A previous patient study reported
that reduced FA in the left Arcuate was related to the degreeof syntactic deficits in patients who suffered from primary
progressive aphasia with some variants (Wilson et al., 2011),
while other studies have claimed other functional roles, suchas phonemic processing, lexico-semantic processing, or wordlearning (Glasser and Rilling, 2008; López-Barroso et al., 2013).
As regards the ventral pathway, a previous functional/diffusion
MRI study has suggested that the left ventral pathway, including
the EmC, MdLF and ILF, is the sound-to-meaning mapping
stream, focusing on regions activated for the contrast of
meaningful speech vs. pseudospeech (Saur et al., 2008), and wehave previously suggested that the left ventral IFG is involved in
the selection and integration of semantic information (Homae
et al., 2002). In our recent study of both normal and agrammaticpatients, we demonstrated the existence of three syntax-related
networks (Kinno et al., 2014): Network I includes the left dorsal
IFG and a portion of the right dorsal pathway, and subserves
syntax and its supportive system, and Network II includes a
portion of the left dorsal pathway, working as a syntax andinput/output interface, while Network III includes a portion of‘‘Materials and Methods’’ Section). Another potential concern
was the thresholding used in the probabilistic tracking, inthat there is no gold standard for the thresholding value.
For connectivity probability values in each voxel, we useda threshold of 0.2% against individual waytotal values (i.e.,values of the number of total successful streamlines; Galantucci
et al., 2011; Ohta et al., 2013). Some studies used a fixed
percentage [e.g., 0.001% in Griffiths et al. (2013)] against the
number of all generated streamlines, which differed individually
due to the different seed size for each participant. Given thatmost of the generated streamlines are rejected when reachingexclusion masks, or when going away from waypoint masks, thethresholding with successful streamlines would be more reliable.
For determining an appropriate thresholding value, it is also
important to consider the decrease in connectivity probability
with distance from the seed mask (Morris et al., 2008). Such
distance effects, as well as the concern that participants withlower waytotal values might result in a very low threshold, should
be properly handled in future studies. Advances in thresholding
would also contribute to examine the individual branchingpatterns, and to identify the anterior target regions, which might
explain individual language abilities. The ROIs in the present
study may depend on the gross anatomy of each tract, and further
research using appropriate methods for quantifying individual
variabilities in such features as branching patterns and curvature
will be needed to understand the functions of language-related
pathways.
Our previous studies using the same Syn and Spe tasks in
L2 have shown that the activations in the dorsal F3t of the left
IFG, as well as the leftward lateralization of this region’s volume,
were correlated with the accuracy of the Syn task (Nauchi andSakai, 2009; Sakai et al., 2009). We have also shown that theleft IFG is specialized in syntactic processing in both L1 andL2 (Sakai et al., 2004, 2009; Tatsuno and Sakai, 2005). In the
Syn-Spe contrast, the left middle temporal gyrus and angulargyrus were also significantly activated (Sakai et al., 2009). In thepresent study, we further showed that the structural propertyof the left Arcuate connecting the IFG and parietotemporal
regions was correlated with the accuracy of the Syn task. The
correlation coefficient between the RTs of Syn and Spe wassignificantly larger than that between the accuracy of the twotasks, indicating that the accuracy reflected distinct abilities
required in each task, while the RTs reflected general cognitive
factors, such as task difficulty and reading proficiency, common
to both tasks. Moreover, short-term memory was not requiredin the present tasks. Although it is generally difficult to exclude
the effects of semantic knowledge from any tasks using naturalsentences, the ungrammatical sentences cannot be judged asincorrect by semantic information alone (see ‘‘Stimuli and Tasks’’Section). Other remaining factors were further excluded fromour partial correlation analyses between the accuracy of Syn
and FA, by controlling the accuracy of Spe, because sentence
reading was common to both tasks. Regarding the relationships
between general cognitive abilities and L2 abilities, a previous
study has reported that L2 reading proficiency was related toperformances in a paired-associate test for unrelated word pairs
and a phonological short-term memory task; this study used

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Dorsal Pathway and Syntactic Abilities

was not. A previous MRI study on the gray matter of twins
showed stronger shared genetic influences on the density (theproportion of gray matter) of the left IFG, which were more
prominent than the genetic influences on the right IFG density(Thompson et al., 2001). Although twin studies on L2 acquisition
are limited, a recent study with twins aged 14 has reported that
25% of the variance of L2 was due to non-shared environmental
factors, where their L2 performances were evaluated by the UK
National Curriculum rating system (Dale et al., 2012). Here we
report that the effects of shared factors depended differentiallyon the L2 tasks, the accuracy, and RTs. More specifically, thecorrelations within twin pairs were strikingly high for RTs ofboth Syn and Spe, which reflected the speed of word recognition,
reading, and/or decision making. Moreover, the high correlations
for the accuracy/RTs of Spe, as well as those for verbal fluency
in L1, suggest that such word-level knowledge is also strongly
associated with shared genetic/environmental factors, while themarginal correlation for the accuracy of Syn suggests thatsyntactic abilities are less prone to such shared factors.
A recent diffusion MRI study has reported that FA varies
along a tract (Johnson et al., 2014). Another study dissected
the Arcuate into three segments (long, anterior, and posterior
segments), and reported that genetic effects on FA were highfor the left long, right anterior, and left posterior segments(Budisavljevic et al., 2015). Our present study employed semiautomatic analyses for the diffusion MRI data, thereby examining
FA in consistent portions of the tracts, which were not possible
in previous studies using the mean FA averaged in the entire
tracts. Although the sample size of twin pairs was too small todraw a stronger conclusion, elaborate studies like the present
study would indicate future directions for large-scale studies.
Large-scale projects with hundreds of twins, such as Enhancing
Neuroimaging Genetics through Meta-Analysis (ENIGMA) andHuman Connectome Project (HCP), have reported that FA
of major tracts was highly heritable (Sprooten et al., 2014;
Kochunov et al., 2015). Our present results did not show
strong correlations of FA among the twin pairs, but thisdiscrepancy may be due to small sample size. Further studies
recruiting both monozygotic and dizygotic twins with better
tractography analyses would clarify the contribution of genetic
and environmental factors to the language-related pathways.
Moreover, methodological improvements on thresholding with
less-manual adjustments in seed selection are expected toincrease the reliability of diffusion MRI analyses. In the present
study, we suggest that some task-related skills and word-levelknowledge are largely associated with shared factors between
twins, whereas individual syntactic abilities in L2 are less prone
to shared factors. We also suggest that the thickness of the left
Arcuate may be more associated with shared factors than FA was.
Our present results further raise an important question regarding
how L2 acquisition and general development in juvenile ages aredifferentially related to the plasticity of the left Arcuate.

the left ventral pathway, and is related to the syntax-semanticinteraction. As demonstrated by the present study, the left
Arcuate, i.e., the left dorsal pathway of Network II, has morefunctional significance among these syntax-related pathways, inthat this pathway’s FA reflected the individual syntactic abilitiesin L2. The left dorsal pathway would probably be involvedin both bottom-up and top-down processing between lexicosemantic information and syntactic computation, consistent withpreviously proposed models (Hagoort, 2005; Friederici, 2012).
Several models for the functions of dorsal and ventral
language pathways have been proposed. For instance, Friederici
and others have proposed that the ventral pathways support
syntactic phrase structure building, while the dorsal pathways,
especially the one connecting the BA 44 and temporal regions,
support the processing of syntactically complex sentences(Friederici, 2012). Hagoort and others have proposed anothermodel, in which two crucial components of language systems,
i.e., Memory (the mental lexicon) and Unification (the online assembly of lexical elements into larger structures atsyntactic, semantic, and phonological levels), are represented
in the temporal/inferior parietal regions and the left IFG,
respectively, connected by the dorsal and ventral pathways(Hagoort, 2014). Bornkessel-Schlesewsky et al. (2015) proposedthat the ventral pathways are engaged in time-independent
auditory object recognition and combination, whereas the dorsal
pathways are engaged in time-dependent sequence processing.
Our results support the critical role of the dorsal pathway
in the light of processing argument structures of verbs in
L2 acquisition. Further research with standardized tests for
performance in English as a foreign language that additionallycover other domains of syntactic ability is needed to reveal how
various subcomponents of syntactic processing are subserved by
the dorsal and ventral pathways. Any approaches to combine
the anatomical significance of these dorsal/ventral pathways
with findings from developmental and lesion studies would
be useful in elucidating how particular pathways contribute to
subprocesses in language functions.
According to a previous study on 9-year-old monozygotic anddizygotic twins, the contribution of non-shared environmental
factors to individual variations of FA, which was averaged in
the entire left SLF (probably extended to the Arcuate), wasapproximately 65%, while the contributions of genetic andshared environmental factors were 30% and 5%, respectively(Brouwer et al., 2010). Consistent with such contribution of nonshared environmental factors, the local FA can increase after
several months of skill or vocabulary training even in adults(Scholz et al., 2009; Hosoda et al., 2013). On the other hand,
it has been reported that the white matter volume was more
dependent on genetic factors; the genetic contribution to thewhole white matter volume was 85%, while the contribution ofgenetic factors to its FA was as low as 25% (Brouwer et al., 2012).
There are thus at least three independent issues that should beclarified for twin studies in neuroscience: (1) shared vs. nonshared genetic/environmental factors; (2) local vs. whole brain;and (3) gray vs. white matter. In the present study, we showedthat the mean thickness in the ROI of the left Arcuate was
highly correlated within monozygotic twin pairs, while its FA

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AUTHOR CONTRIBUTIONS
All authors listed, have made substantial, direct and intellectual
contribution to the work, and approved it for publication.

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Dorsal Pathway and Syntactic Abilities

ACKNOWLEDGMENTS

supporting the present study. This work was supported by a Core
Research for Evolutional Science and Technology (CREST) grantfrom the Japan Science and Technology Agency (JST) and by aGrant-in-Aid for the Japan Society for the Promotion of Science(JSPS) Fellows (Grant Number 15J09382).

We thank R. Kinno and S. Ohta for their helpful discussions,N. Komoro for her technical assistance, H. Matsuda for her
administrative assistance, and all students/guardians/teachers for

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Copyright © 2016 Yamamoto and Sakai. This is an open-access article distributed
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June 2016 | Volume 10 | Article 295