A Surface-based Analysis of Language Lateralization - Am MIT spezialisierte KI-Forschung

A Surface-based Analysis of Language Lateralization
and Cortical Asymmetry

Douglas N. Greve1, Lise Van der Haegen2, Qing Cai2,3,4,
Steven Stufflebeam1, Mert R. Sabuncu1,5,
Bruce Fischl1,5, and Marc Brysbaert2

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Abstrakt

■ Among brain functions, language is one of the most lateral-
isiert. Cortical language areas are also some of the most asymmet-
rical in the brain. An open question is whether the asymmetry in
function is linked to the asymmetry in anatomy. Um das zu erwähnen
question, we measured anatomical asymmetry in 34 Teilnehmer
shown with fMRI to have language dominance of the left hemi-
Kugel (LLD) Und 21 participants shown to have atypical right
hemisphere dominance (RLD). All participants were healthy
and left-handed, and most (80%) were female. Gray matter
(GM) volume asymmetry was measured using an automated
surface-based technique in both ROIs and exploratory analyses.
In the ROI analysis, a significant difference between LLD and
RLD was found in the insula. No differences were found in planum
temporale (PT), pars opercularis (POp), pars triangularis (PTr),
or Heschlʼs gyrus (HG). The PT, POp, insula, and HG were all
significantly left lateralized in both LLD and RLD participants.
Both the positive and negative ROI findings replicate a pre-
vious study using manually labeled ROIs in a different cohort

[Keller, S. S., Roberts, N., Garcia-Finana, M., Mohammadi, S.,
Ringelstein, E. B., Knecht, S., et al. Can the language-dominant
hemisphere be predicted by brain anatomy? Zeitschrift für Kognition
Neurowissenschaften, 23, 2013–2029, 2011]. The exploratory analysis
was accomplished using a new surface-based registration that
aligns cortical folding patterns across both subject and hemi-
Kugel. A small but significant cluster was found in the superior
temporal gyrus that overlapped with the PT. A cluster was also
found in the ventral occipitotemporal cortex corresponding to
the visual word recognition area. The surface-based analysis also
makes it possible to disentangle the effects of GM volume, dick-
ness, and surface area while removing the effects of curvature. Für
both the ROI and exploratory analyses, the difference between
LLD and RLD volume laterality was most strongly driven by differ-
ences in surface area and not cortical thickness. Gesamt, Dort
were surprisingly few differences in GM volume asymmetry
between LLD and RLD indicating that gross morphometric asym-
metry is only subtly related to functional language laterality. ■

EINFÜHRUNG

It has long been known that speech production is later-
alized in the brain, with the left side being dominant in
most people. On the basis of a review of the existing clin-
ical data, Benson and Geschwind (1985) estimated that
60% of right-handed patients with unilateral left hemi-
Kugel (LH) lesions developed aphasia, whereas only
2% of right-handed patients with right hemisphere (RH)
damage did. For left-handed patients, the figures were,
jeweils, 32% Und 24%. Other clinical evidence came
from the Wada test (Wada & Rasmussen, 1960). In diesem
test, sodium amytal is injected unilaterally to anesthetize
a hemisphere, and the effects on speech production are
monitored. The test was administered to determine
speech dominance in epileptic patients before surgery.
Loring et al. (1990) described the outcome of one of
the best controlled studies. A total of 103 Patienten (91
right-handed and 12 links- or mixed-handed) were tested.

1Harvard Medical School, 2Ghent University, 3East China Nor-
mal University, 4INSERM, Cognitive Neuroimaging Unit, Frankreich,
5Massachusetts Institute of Technology

Of these, 79 had exclusively LH language representation
(73 right-handers) involving both production (counting)
and comprehension (following simple commands). Nur
two patients had exclusive RH language representation
(1 right-hander). The remaining 22 Teilnehmer (17 Rechts-
handers) had performance decrements after injection to
each hemisphere.

In den 1990ern, brain imaging started to replace the Wada
test. Classifications with these techniques were shown to
be nearly identical to those of the Wada test (z.B., Binder
et al., 1996) and made possible research on healthy indi-
viduals. Using transcranial Doppler sonography, Knecht
and colleagues reported that 95% of right-handers had
a larger increase in blood flow in the LH when silently
generating words starting with a particular letter. Für
the left-handers, the percentage of left dominance was
75–90%, depending on the degree of handedness (Van der
Haegen, Cai, Seurinck, & Brysbaert, 2011; Knecht et al.,
2000; Loring et al., 1990).

Kürzlich, researchers have started to investigate what
consequences atypical right speech lateralization has for
the lateralization of other brain functions. Erste, es war

Zeitschrift für kognitive Neurowissenschaften 25:9, S. 1477–1492
doi:10.1162/jocn_a_00405

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shown that right lateralization of Brocaʼs area is mostly,
but not always, accompanied by atypical lateralization of
the occipito-temporal regions involved in word reading
(Van der Haegen, Cai, & Brysbaert, 2012; Seghier & Price,
2011; Cai, Paulignan, Brysbaert, Ibarrola, & Nazir, 2010;
Cai, Lavidor, Brysbaert, Paulignan, & Nazir, 2008). Zweite,
it was observed that all participants with RH speech
dominance were right dominant for tool use as well
(Vingerhoets et al., 2013), independent of handed-
ness. Good performance on this task involved the SMA
(BA 6), Brocaʼs area (BA 44/45), the dorsolateral pFC
(BA 9/46), and the posterior parietal cortex (BA 40/7).
Endlich, Cai, Van der Haegen, and Brysbaert (2013) Re-
ported that all participants with right speech lateralization
had atypical, LH lateralization of the frontoparietal network
involved in visuospatial processing (the Landmark task).
This network involved the inferior parietal sulcus and the
superior parietal lobule (BA 39/40) together with the FEF
(the intersection of BA 4/6/8) and the inferior frontal sulcus
(BA 44/45) in the nondominant hemisphere. Daher, Es
appears that atypical speech lateralization has profound
implications for functional lateralization throughout the
brain.1

In this study, we examine how atypical speech domi-
nance is related to the structural asymmetries of the
cerebral cortex. Given the large functional implications,
one might expect that atypical speech lateralization is
accompanied by substantial differences in gray matter
(GM). A dominant hypothesis is that the usual LH language
dominance may be the result of the LH being larger in
brain areas related to language processing. In der Tat, beide
the planum temporale (PT), involved in auditory language
Verarbeitung, and the opercular part of Brocaʼs area are
known to show a leftward volume asymmetry, welches ist
larger in right-handers than in left-handers (Toga &
Thompson, 2003). This discovery has led to a number
of studies in which functional laterality scores were corre-
lated with anatomical laterality scores (z.B., Josse, Kherif,
Flandin, Seghier, & Price, 2009; Josse, Mazoyer, Crivello,
& Tzourio-Mazoyer, 2003; Tzourio, Nkanga-Ngila, &
Mazoyer, 1998). A limitation of these studies, Jedoch,
was that they usually involved small numbers of partici-
pants who were not selected for hemisphere dominance.
Given the rareness of atypical right speech laterality, es ist
unlikely that enough participants with truly inversed
speech dominance were included to draw valid conclu-
sionen (see Cai et al., 2013, for more information on the
problem of drawing conclusions on the basis of samples
mainly consisting of left language dominant participants).
Arguably the best study comparing left language domi-
nant (LLD) participants with right language dominant
(RLD) participants was Keller et al. (2011). The authors
compared the anatomical MRI images of 15 LLD partici-
pants with those of 10 RLD participants (language dom-
inance determined by transcranial Doppler sonography
or fMRI during a silent word generation [WG]). To their
surprise, Keller et al. observed no relationship between

volume asymmetry of Brocaʼs area or the PT and lan-
guage dominance (the trends were even in the opposite
Richtung, with larger leftward asymmetries for RLD par-
ticipants than for LLD participants). The only region for
which they found a robust relationship between volume
asymmetry and language dominance was the insula, mit
a tendency toward rightward asymmetry for the RLD par-
ticipants, making the authors conclude that the insular
morphology should be given more importance in studies
on the anatomical correlates of human language laterali-
zation. This study performs an analysis similar to Keller
et al. (2011) to see whether the surprising findings can
be replicated on new groups of LLD and RLD participants
and to what extent the results depend on the methodol-
ogy used.

An important issue in studying the relationship be-
tween structure and function is the delineation of mor-
phological features. Typically, cortical structures known
to be involved in language are manually segmented
based on landmarks to obtain measures of volume, Bereich,
Länge, and/or angulation. Jedoch, the cortical surface
is highly folded with variable thickness, and there is a
large degree of anatomical variability across participants.
This variability makes it difficult to consistently and accu-
rately define and quantify structures of interest. Manual
labeling is also a tedious procedure that is not feasible
in the large data sets required for testing hypotheses in
human studies.

Even in the case where morphological features are well
defined and quantified, they may still be somewhat arbi-
trary relative to the underlying function. The anatomical
manifestation of a functional asymmetry may not confine
itself neatly within a morphological feature chosen by an
anatomist. This can cause a loss of statistical power if the
entire feature is considered. In this case, exploratory
methods may be more powerful because they allow the
data itself to determine what the underlying morphologi-
cal feature should be. Voxel-based morphometry ( VBM;
Aschenbrenner & Friston, 2000) is often used in this type of
application, including brain asymmetry (Luders, Gaser,
Jancke, & Schlaug, 2004; Watkins et al., 2001). Dort
has only been one study using VBM to assess structural
asymmetry differences in LLD and RLD (Dorsaint-Pierre
et al., 2006). This VBM study was in an epilepsy pre-
surgical cohort involving a small number of patients (11 von
whom were RLD), which may not reflect the general
Bevölkerung. Zusätzlich, VBM has two other short-
kommt. Erste, it relies on volume-based registration to
align brains across participants, which is often inaccurate
when attempting to align particular cortical folds (Fischl
et al., 2008). Zweite, when differences are found with
VBM, the results cannot be refined to determine
whether the underlying cause of the difference was be-
cause of differences in folding pattern, GM volume, cor-
tical surface, or cortical thickness.

This study addresses the limitations of Keller et al.
(2011) in three important ways. Erste, it uses a relatively

1478

Zeitschrift für kognitive Neurowissenschaften

Volumen 25, Nummer 9

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large sample of healthy participants who have been ver-
ified to be strongly RLD based on fMRI (n = 21 instead of
10), thereby considerably increasing the power of the
Studie. This increase was made practical through the
use of a low-cost and noninvasive screening procedure
developed previously (Van der Haegen et al., 2011).
Zweite, it uses computer-generated mesh models of
the cortical surface to automatically find and accurately
quantify standard morphological features, einschließlich
those associated with language. Endlich, it uses surface-
based interparticipant and interhemispheric registration
to align folding patterns so that the cortical volume,
surface area, and thickness can be consistently evaluated
across language dominance group, Thema, and hemi-
Kugel. This registration allows for an exploratory analy-
sis in which the data dictate the anatomical boundaries
of areas that show consistent structural and functional
asymmetries.

METHODEN

Laterality Index

The laterality index (LI) is used to quantify the difference
between left and right while removing the effects of brain
Größe. We use the formula:

LI ¼

ðL − RÞ
ðL þ RÞ

ð1Þ

where L is the value from the left side and R is the value
from the right side. LI has a range from −1 (vollständig
right lateralized) Zu +1 (completely left lateralized). Das
formula can be applied to any metric including volume,
Bereich, Dicke, volume of functional activation, or behav-
ioral measure.

Participants and fMRI Analysis

Full details of subject recruitment, handedness assess-
ment, screening procedures, fMRI analysis, and language
LI calculation are provided in Van der Haegen et al.
(2011, 2012). For completeness, we summarize them
Hier. All participants signed an informed consent form
according to the guidelines of the Ethics Committee of
the Ghent University Hospital. A total of 269 partici-
pants2 were accepted to the initial screening based
on the criteria that they wrote and drew with their
left hand to increase the likelihood of atypical language
dominance. Handedness was later assessed with the
Edinburgh Handedness Inventory (Oldfield, 1971) modi-
fied to have answers in the range of −3 to −1 (degree of
left-handedness) oder +1 Zu +3 (right-handedness). Most
participants underwent two visual half field ( VHF) tasks
(Hunter & Brysbaert, 2008) in which they were asked to
name words and pictures presented to the left visual field
(LVF) or to the right visual field (RVF). LIs were calculated

by subtracting the mean RT to stimuli in RVF from the
mean RT to stimuli in LVF.

Sixty-five participants were invited (and willing) to take
part in the fMRI study. Twenty-five were expected to be
LLD on the basis of their VHF scores; the remaining 40
had an LVF advantage on one of the VHF tasks and were
hoped to be RLD (Van der Haegen et al., 2011; Hunter &
Brysbaert, 2008).3 The fMRI task consisted of silent WG
(Hunter & Brysbaert, 2008; Knecht et al., 2000; Pujol,
Deus, Losilla, & Capdevila, 1999). Participants were asked
to silently think of as many words as possible, beginning
with a cued letter. The control/baseline condition was
silent repetition of the nonword “baba.” SPMs were gen-
erated based on target letter versus nonword contrast.
The functional LIs were computed in areas approximately
corresponding to Brocaʼs area (d.h., BA 44 and BA 45; AAL
Vorlage; Tzourio-Mazoyer et al., 2002). These regions
were chosen because they are the most active areas in
the silent WG task and are known to be involved in many
linguistic functions (Heim, Eickhoff, & Amunts, 2008;
Amunts et al., 2004).

For statistical analysis, Die 65 participants were cate-
gorized into three groups based on the functional WG
LI scores: LLD if LI > 0.6, RLD if LI < −0.6, and bilateral language dominant (BLD) otherwise. This categorization was used to make a clear separation between the RLD group and the LLD group (see the Discussion for the rea- soning behind this model). Figure 1 shows the distribu- tion of the fMRI LIs of all participants. The demographics and mean LI for the three groups are shown in Table 1. The handedness scores for all groups were less than −2, indicating strong left handedness (−3 would be the most extreme for left handers). The groups did not signifi- cantly differ in handedness ( p > .55) or age ( p > .48).
The sample was recruited from a wide range of courses at
university or higher education schools. As female stu-
dents seemed to be more willing to take part, Sie
formed the majority of participants.

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Figur 1. Distribution of the functional LI from the WG task for
all participants. The horizontal dashed lines indicate the threshold of
±0.6. The vertical dashed lines indicate the categorical boundaries
of LLD, BLD, and RLD.

Greve et al.

1479

Tisch 1. Participant Demographics

Group

Total

Male

Female

Alter (SD)

Handedness (SD)

fMRI LI (SD)

Template

LLD

RLD

BLD

20.4 (2.6)

20.9 (2.8)

21.0 (1.7)

−2.12 (1.06)

−2.35 (0.78)

−2.01 (1.36)

+0.78 (.09)

−0.84 (.11)

+0.14 (.43)

A second fMRI task (lexical decision task, LDT) War
also collected on these participants (Van der Haegen
et al., 2012). The LDT assesses the lateralization of word
reading by looking at activity in the vOT. Stimuli con-
sisted of high- and low-frequency words, consonant
strings, and pixel-scrambled words. Participants were re-
quired to respond with button press as to whether the
stimulus was a word or nonword. Cerebral dominance
was determined on the basis of the WG task and not
the LDT, in line with clinical practice (Spreer et al.,
2002; Benson et al., 1999; Springer et al., 1999), welche
uses speech production lateralization as an index of lan-
guage laterality. The brain areas involved in speech pro-
duction are the most lateralized, arguably because fluent
speaking requires a single control center (Kosslyn, 1987).

MRI Acquisition

Images were acquired on a 3-T Siemens Trio MRI scanner
at Ghent University (Siemens Medical Systems, Erlangen,
Deutschland) with an eight-channel radiofrequency head
coil. A high-resolution anatomical image was collected
using a T1-weighted 3-D MPRAGE sequence. All images
war 256 × 256 × 176 with voxel size = 0.9 × 0.9 ×
0.9 mm3. The data were acquired with either one of
two sets of pulse sequence parameters: repetition time =
1550 ms, Echozeit = 2.39 ms, flip angle = 9°, inver-
sion time = 900 ms, pixel frequency = 180 Hz; oder
repetition time = 2530 ms, Echozeit = 2.58 ms,
flip angle = 7°, inversion time = 1100 ms, pixel fre-
quency = 190 Hz. Most (n = 54) were acquired with
the first set. The proportion of each parameter set was
the same (85% Zu 15%) in the LLD and RLD groups.

Anatomical Analysis

All participants were analyzed in FreeSurfer (www.surfer.
nmr.mgh.harvard.edu, Ausführung 5.1) to provide detailed
anatomical information customized for each participant
(Dale, Fischl, & Sereno, 1999; Fischl, Sereno, & Dale,
1999). The FreeSurfer analysis stream includes intensity
bias field removal, skull stripping, and assigning a neuro-
anatomical label (z.B., hippocampus, amygdala, usw.) Zu
each voxel (Segonne et al., 2004; Fischl et al., 2002). In
addition to the volume-based analysis, FreeSurfer con-
structs models of the cortical surface. A surface model
consists of a mesh of triangles. The location of the mesh
is controlled by adjusting the location of the vertices. A

vertex is the place where the points of neighboring tri-
angles meet (typically about 1 mm apart). The vertex
positions are adjusted such that the surface follows the
T1 intensity gradient between cortical white matter
(WM) and cortical GM. Smoothness constraints allow the
surface to cut through a voxel to model partial volume
effects and provide subvoxel accuracy of the location of
the surface. This highly folded surface can be “inflated”
(Figur 2) to see inside the sulci. A second surface is also
fit to the outside of the brain (between the GM and the
pia). The first surface is called the “white” surface, und das
second is called the “pial” surface (Figures 3A and 4A show
pial surfaces). The LH and RH are modeled separately. Alle
surfaces are constructed in the native anatomical space.

Cortical GM Metrics

In FreeSurfer, the distance between the white and pial
surfaces at a vertex is defined to be the cortical thickness
at that vertex (Fischl & Dale, 2000). The area of a vertex is

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Figur 2. Significance map of volume LI difference between LLD
and RLD rendered on the inflated cortical surface of the symmetric
Vorlage (uncorrected vertex-wise threshold p < .01). Red/yellow indicates that LLD > RLD; blue/cyan indicates that LLD < RLD. The yellow outlines indicate the boundaries of the a priori ROIs (ALA and PLA). (A) Results from the template initialized with the LH. (B) Results from the template initialized with the RH. From top to bottom: (1) lateral view, (2) inferior view, (3) medial view. 1480 Journal of Cognitive Neuroscience Volume 25, Number 9 Figure 3. (A) View of the left posterior sylvian fissure including the STG cluster on the folded symmetric template pial surface (same data as in Figure 2A1). (B) LI results broken down by subject group and volume, area, and thickness (with standard deviation bars across subject) based upon averages from within the exploratory STG cluster. The p values are for post hoc tests of laterality or differences in laterality between LLD and RLD. The p values have been corrected for the eight post hoc tests. All p values are based on a two-sided t test with 53 DOF. defined as the average area of the triangles of which the vertex is a member. The GM volume of a vertex is defined as the area times the thickness. The surface area of a region can be computed by adding up the area of the ver- tices in that region, and the same can be done to com- pute the GM volume. The surface curvature at a vertex can be computed based on its spatial relationship to neighboring vertices. The curvature is a quantification of the folding patterns. Thus, at each point along the sub- jectʼs surface, the thickness, area, volume, and curvature can be quantified, all with subvoxel accuracy. rate for cortical areas (Fischl et al., 2008). The thickness, area, and volume can be resampled into the standard space so that vertex-wise comparisons across subjects can be made in an exploratory analysis. The area and volume resampling in- cludes a “jacobian correction“ (similar to that described in Winkler et al., 2012) to account for any stretching or compres- sion in the registration. This correction is not needed for thickness because it is measured along a vector normal to any stretching or compression. Cross-hemisphere Symmetric Registration Template Surface-based Intersubject Registration The curvature is used to drive a nonlinear surface-based inter- subject registration procedure that aligns the cortical folding patterns of each subject to a standard surface space (Fischl, Sereno, Tootell, & Dale, 1999). This approach is similar to performing a volume-based registration to Talairach or Montreal Neurological Institute space but is much more accu- There are two standard space surface registration templates in FreeSurfer, one for each hemisphere. Simply registering a hemisphere to a contralateral template can create biases, that is, one will get different results depending upon whether one registers both hemispheres to the LH template or to the RH template. A symmetric template is needed, which can be generated in one of two ways: (1) regenerate D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 5 / 9 2 5 1 / 4 9 7 / 7 1 1 4 9 7 4 7 5 / 5 1 6 7 0 7 o 9 c 4 n 2 _ 5 a / _ j 0 o 0 c 4 n 0 5 _ a p _ d 0 0 b 4 y 0 g 5 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / f . t Figure 4. (A) vOT cluster shown on the pial surface of the LH (view is inferior and slightly lateral). The green outline is the lateral occipito- temporal gyrus from the Destrieux atlas. This is the same data as in Figure 2A2. (B) LI results broken down by subject group and volume, area, and thickness (with standard deviation bars across subject) based upon averages from within the vOT cluster. The p values are for post hoc tests of laterality or differences in laterality between LLD and RLD. The p values have been corrected for the eight post hoc tests. All p values are based on a two-sided t test with 53 DOF. u s e r o n 1 7 M a y 2 0 2 1 Greve et al. 1481 the template from scratch by left–right reversing the sub- jects used in the template and reregistering with all data (i.e., treating the reversed subjects as new, additional sub- jects) or (2) left–right reversing the template itself and aver- aging with the non reversed atlas. This second approach is easy to implement and dominants in the field ( Josse et al., 2009; Eckert et al., 2008; Watkins et al., 2001). However, this approach has several drawbacks, namely, a blurrier atlas is created because features are washed out, and there is no way to keep track of the amount of variance because of variability across hemispheres. For these reasons, we cre- ated a new surface template from scratch using an iterative technique described in Fischl, Sereno, Tootell, et al. (1999) modified to yield a symmetric template. The symmetric template was created using 42 participants from this study consisting of an equal number of LLD and RLD, each group represented by 3 men and 18 women. No BLD were used in the template construction. First, an initial template was cre- ated from only the LHs of these participants based on the registration to the LH standard template. Both LHs and RHs from all participants were aligned with this initial LH template. A new template was then created from these 84 surfaces, and the surfaces were reregistered to it. This new template is a mixture of LH and RH and so less biased than the initial LH-only template. This process was repeated 35 times to remove the influence of initializing with LH. To test whether this procedure leads to a symmetrical tem- plate, the entire template creation procedure was replicated initialing with the RH. The exploratory analysis described below was performed using both the LH- and RH-initialized templates. Similarity in the results is evidence of a sym- metric template. Anatomically Defined Cortical ROIs FreeSurfer has the ability to automatically label the cortex in a way intended to replicate the labeling of a trained anatomist (Destrieux, Fischl, Dale, & Halgren, 2010; Desikan et al., 2006; Fischl et al., 2004). This labeling goes beyond simply the mapping of an ROI atlas into the subject space because the boundaries of the labels are customized to each participant based on curvature statistics stored in the ROI atlas. Note that, whereas an anatomist uses explicit rules and landmarks to define specific areas, the automatic procedure relies only on implicit rules generated during the training process. This method has been validated using a jackknifing proce- dure (Fischl et al., 2004). We used the Destrieux atlas (Destrieux et al., 2010), which has 74 ROIs on each hemi- sphere and was derived from 12 participants not involved in this study. We selected five language-related ROIs from which to report the volume, area, and thickness (see Destrieux et al., 2010, for details on the landmarks used to define these ROIs): (1) opercular part of the inferior frontal gyrus (pars opercularis, POp), (2) triangular part of the inferior frontal gyrus (pars triangularis; PTr), (3) PT, (4) anterior transverse temporal gyrus (Heschlʼs gyrus, HG), (5) insula composed of superior, anterior, and inferior segments of the circular sulcus of the insula, short and long insular gyri, and the central sulcus of the insula. The PT is shown in an inflated hemisphere in Figure 5. We emphasize that the anatomical ROIs are independent of the interhemi- spheric registration. Note that the Destrieux PT definition includes both the posterior horizontal segment and the posterior ascending ramus (sometimes referred to as the planum parietale or PP). These ROIs were chosen for several reasons. POp and PTr approximate Brocaʼs area. PT has prominent anatomic asymmetries and is an area of language function. The insula has been found to have an interaction between anatomy and language function (Keller et al., 2011). HG was chosen because of known anatomical asymmetry (Dorsaint-Pierre et al., 2006; Penhune, Cismaru, Dorsaint-Pierre, Petitto, & Zatorre, 2003; Penhune, Zatorre, MacDonald, & Evans, 1996; Rademacher, Caviness, Steinmetz, & Galaburda, 1993). Figure 5. Method used to geometrically construct an ROI to cover the STG cluster. (A) The PT was divided into three sections along its long axis (green–brown–green labels). The lateral aspect of the STG (LASTG) was divided into five sections (pink–blue– pink labels). (B) The second section of the PT and the first section of the LASTG are combined to cover the STG cluster fairly well (orange outline). The significance map is the same as in Figure 2A1 and Figure 3. 1482 Journal of Cognitive Neuroscience Volume 25, Number 9 D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 5 / 9 2 5 1 / 4 9 7 / 7 1 1 4 9 7 4 7 5 / 5 1 6 7 0 7 o 9 c 4 n 2 _ 5 a / _ j 0 o 0 c 4 n 0 5 _ a p _ d 0 0 b 4 y 0 g 5 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / . t f u s e r o n 1 7 M a y 2 0 2 1 Performing an anatomical ROI analysis also provides a means through which the FreeSurfer ROI results can be compared with those of other studies as well as with the exploratory analysis. Exploratory Spatial Analysis Both hemispheres of all 65 participants were registered to the symmetric template, and the vertex-wise cortical GM volume maps were mapped into the symmetric stan- dard space. These were then surface-smoothed by 10 mm FWHM. For each participant, the volume LI at each vertex was computed as per Equation 1. A statistical analysis was performed at each vertex to evaluate the difference be- tween the LLD and RLD participants using a two-group un- signed t test with 53 degrees of freedom (DOF). Clusters were defined as groups of contiguous vertices with vertex- wise p < .01 (the cluster forming threshold). The p value for a cluster was determined through Monte Carlo simula- tion in which white Gaussian noise was repeatedly syn- thesized on the surface, spatially smoothed, thresholded, and clustered to determine the distribution of cluster sizes under the null hypothesis (Hagler, Saygin, & Sereno, 2006). The cluster search space was constrained by two a priori regions corresponding to an anterior language area (ALA) that surrounds Brocaʼs area and a posterior language area (PLA) that surrounds Wernickeʼs area, which are often mentioned as traditional language areas (Toga & Thompson, 2003). We defined the ALA by combining four of the Destrieux ROIs: (1) POp, (2) PTr, (3) horizontal and vertical ramus of the anterior segment of the lateral sulcus, and (4) inferior part of the precentral sulcus. We defined the PLA by combining Destrieux ROIs: (1) angular gyrus, (2) supramarginal gyrus, (3) PT, (4) transverse temporal sulcus, (5) posterior ramus of the lateral sulcus, (6) lateral aspect of the superior temporal gyrus (STG; posterior half ), (7) STS (posterior two thirds), and (8) sulcus inter- medius primus of Jensen. The resulting ALA and PLA are shown in yellow outline in Figure 2. Because this is an exploratory analysis, the boundaries for these areas were selected to be rather large so as to reduce the chance of missing clusters. The ALA and PLA are larger and different than the anatomical ROIs above so that the exploratory analysis might allow the data to define new ROIs that are not necessarily defined by landmarks visible to an anato- mist. A cluster analysis using the whole hemisphere was also performed to search for any clusters that might be outside of traditional language areas. RESULTS Anatomically Defined ROIs Figure 6 shows the average volume LI for the three groups. The PT, POp, insula, and HG where all highly significantly left-lateralized in both LLD and RLD ( p < .0001). Post hoc analysis revealed that each ROI was significantly left Figure 6. Mean volume LIs for anatomically defined ROIs (with standard deviation bars). Ins: Insula. lateralized in terms of surface area ( p < .0001), but not thickness. The PTr was the only structure that was not significantly left lateralized ( p = .31). All ROIs showed numerically greater volume LIs for LLD over RLD; however, the only structure to show a significant difference was insula ( p = .0127, after correcting for five comparisons). The PT significance was trending toward a difference ( p = .13, uncorrected). The numerical laterality difference was greater in PT than in insula, but the variability of insula was much smaller leading to a greater effect size. Both groups were left lateralized in insula, but LLD was more so. A post hoc analysis revealed that LLD and RLD differed significantly in insula area ( p = .0127), but not thickness ( p = .82), after correcting for five tests. The BLD group showed roughly the same laterality pattern across the ROIs as the LLD and RLD groups but was neither consistently greater than nor less than LLD or RLD. Exploratory Analysis A whole-hemisphere map of the LLD-RLD difference is shown in Figure 2. The left panel shows the results of the LH-initialized template; the right panel shows the results with the RH-initialized template. Figure 2 is a sig- nificance map thresholded a vertex-wise uncorrected two- sided threshold of p < .01 where red/yellow indicates LLD > RLD and blue/cyan indicates RLD > LLD. Diese
statistics are shown on an “inflated” cortical surface of
the symmetric template where the light gray indicates a
gyrus and the dark gray indicates a sulcus. Within the
a priori regions (yellow boundaries), there is only one clus-
ter in STG that survives multiple comparison correction
(see also Figures 3A and 5B). No significant clusters were
found in the ALA. Outside these areas, there was a large
cluster in the ventral occipitotemporal (vOT) cortex that
survived whole hemisphere correction. See Table 2 for a list
of clusters, their p values, surface areas, and coordinates.
There are some other candidate clusters in Figure 2, Aber
they did not survive correction for multiple comparisons

Greve et al.

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Tisch 2. Significant Clusters in the LLD–RLD Exploratory Analysis

Cluster

Initialization Hemisphere

Cluster p Value

Bereich (mm2)

STG

vOT

.0076

.0094

.0482

.0390

207

187

337

343

Centroid MNI305
Coordinates (xyz)

−58.2

58.6

−36.9

37.8

−39.9

−39.3

−49.6

−49.2

12.7

13.5

−17.8

−17.4

and so are not reported further. Both the maps in Figure 2
and the cluster summaries in Table 2 indicate the results
are robust to initialization hemisphere. For both the STG
and vOT clusters, the coordinates of the centroids between
LH and RH initialization are different by only about 1 mm
(after reversing the sign of the x coordinate).

Post hoc Tests and BLD Results

Post hoc tests were performed on the STG and vOT clus-
ters to determine whether the difference in groups was
being driven by laterality differences in one or both
groups and whether the underlying differences were be-
cause of volume, Bereich, or thickness differences. Abbildung 3B
shows a bar plot of the LIs for the STG cluster volume,
Bereich, and thickness broken down by group along with
p values corrected for eight post hoc tests. One can see
that there is a large difference in volume LI between the
groups; this is expected because this cluster was selected
for a volume LI difference (and why the LLD–RLD p value
for volume LI is not given). The LLD group has a positive
volume LI, and the RLD have a negative volume LI, beide
of which are significantly different than 0. This indicates
that both groups are asymmetric with opposite laterality.
The area LI is significantly different between the groups.
The LLD group has a significantly positive area LI; Die
RLD group has a negative area LI, but it was not signifi-
kippen. The LLD group shows no thickness laterality, Aber
the RLD group has a significantly thicker right cortex than
left in this cluster. There is a thickness LI difference be-
tween groups, but it does not survive correction for mul-
tiple post hoc tests. The results for the BLD group within
this cluster are also shown. On each measure, the BLD
group is between the LLD and RLD groups. We point
out that the BLD results are completely independent of
the participants used to construct the registration tem-
plate and the cluster.

A close-up of the vOT cluster is shown in Figure 4A
along with the post hoc tests in Figure 4B. The volume
is significantly lateralized in both groups in opposite di-
rections. There is a significant difference between groups
in area lateralization but not in thickness. The RLD group
is significantly rightward lateralized for both area and
Dicke. From this data, it appears that the difference
in GM volume LI in the vOT is being driven by area

and not thickness. The BLD group is between the LLD
and RLD in volume and surface area.

Confirmatory Cluster Analysis

The exploratory method developed in this manuscript is
novel, and so we would like to demonstrate that the STG
and vOT clusters are not merely an artifact of the non-
linear registration procedure or resampling of the indi-
vidual volume maps into template space. Im Idealfall, Die
STG and vOT clusters would be labeled independently
in each participant; these labels could then be used to
compute LLD and RLD statistics to verify results from
the exploratory analysis. The problem is to get clusters
in each participant that are independent of the registra-
tion and resampling procedures. We cannot simply map
the cluster from the template space back into the indi-
vidual space because that would be dependent upon
the registration. Stattdessen, we exploit the geometrical
relationship between the clusters and the labels of the
Destrieux atlas. Zum Beispiel, the STG cluster can be
approximated in the following way (siehe Abbildung 5). Erste,
the PT is divided into three sections along its long axis
(the green–brown–green labels in Figure 5A). Nächste, Die
lateral aspect of the STG (LASTG) is divided into five sec-
tionen (the pink-blue-pink labels in Figure 5A). When the
second section of the PT and the first section of the
LASTG are combined into a single ROI (orange outline
in Figure 5B), the STG cluster is covered fairly well.
The same geometric operations can be performed in
each individual brain to get an approximation of the
STG cluster without resorting to the registration used
to create the STG cluster. The results (Figur 7) have a
similar pattern to that of the original cluster analysis.
The volume LI for the LLD is still significantly greater than
that of the RLD ( P < .02), and the BLD is still between the LLD and RLD. The difference is that all LIs are shifted by about 0.13 to be more positive (e.g., RLD is now pos- itive instead of negative). This shift is probably because of the fact that the approximate STG cluster does not cover the actual cluster perfectly. A similar analysis was applied to the vOT cluster with consistent findings. Although the confirmatory ROI is obviously informed by the exploratory map-based analysis, the boundaries of the constructed ROI in each participant are completely independent of 1484 Journal of Cognitive Neuroscience Volume 25, Number 9 D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 5 / 9 2 5 1 / 4 9 7 / 7 1 1 4 9 7 4 7 5 / 5 1 6 7 0 7 o 9 c 4 n 2 _ 5 a / _ j 0 o 0 c 4 n 0 5 _ a p _ d 0 0 b 4 y 0 g 5 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j f t . / u s e r o n 1 7 M a y 2 0 2 1 sures for these participants. These include fMRI of an LDT (Van der Haegen et al., 2012) and three behav- ioral tasks: Dichotic Listening (DL, Van der Haegen, Westerhausen, Hugdahl, & Brysbaert, 2013), VHF task with pictures as stimuli ( VHFP), and VHF task with words as stimuli ( VHFW; Van der Haegen et al., 2011). We computed LIs for each of these behavioral and fMRI measures. We also have the fMRI results for the subvocal WG task; however, the correlation between the WG task LI and the cluster LIs is circular because the WG task was used to assign participants into LLD, RLD, and BLD. To gain further insight into the functional correlates of the anatomical asymmetries, we computed the correlation coefficient between all pairs of LIs. The resulting matrix is shown in Table 3. The behavioral tasks returned the expected asymmetries: a right ear advantage in the DL task for the LLD and a left ear advantage for the RLD, and RVF advantages for LLD versus LVF advantages for RLD. The only a priori defined ROI that correlated with function was the insula, which correlated with ear advantage in the DL task and fMRI laterality in the WG task. The exploratory clusters showed higher correla- tions with functional data: The STG cluster correlated with LDT and VHFP. The vOT cluster correlated with LDT and DL, but, surprisingly, not with the VHF tasks. The correlations in Table 3 are based on all participants; further analyses indicated that the conclusions remain the same if the bilateral participants are omitted or when Figure 7. Comparison of volume LI within the STG (exploratory) cluster and within the (confirmatory) anatomically constructed ROI (with standard deviation bars across subject). The bars for the LLD and RLD are the same as the “Volume” bars in Figure 3. the interhemispheric registration and so confirmatory relative to this registration. Correlations with fMRI and Behavioral Measures In addition to the anatomical information, we also have access to various functional language lateralization mea- Table 3. Correlation Coefficients between Anatomical Asymmetries and Functional Data (Number of Participants in the Lower Left Half ) Anatomical Measures fMRI Behavioral PT 65 65 65 65 65 65 65 52 42 58 58 POp −.16 65 65 65 65 65 65 52 42 58 58 PT POp PTr HG Insula STG vOT WG LDT DL VHFP VHFW PTr HG Insula STG vOT WG −.12* −.05 65 65 65 65 65 52 42 58 58 .11 .12 .10 65 65 65 65 52 42 58 58 −.06 .42** .12 −.03 .15 −.11 −.10 .03 .15* 65 65 65 52 42 58 58 65 65 52 42 58 58 .10 .11 −.00 −.15 .27** .41a 65 52 42 58 58 .17* .16 −.06 .08 .38* .46a .47a 52 42 58 58 LDT .05 .00 −.17 −.17 −.03 .26 .31* .62** 35 48 48 DL −.05 .16 .25 .07 .38* .20 .49** .58** .23 38 38 VHFP VHFW .09 −.03 −.02 .08 .20 .11 .06 .53** .23* .38* .60** .07 .01 −.18 .05 .16 .27* .11 .51** .46** .21 58 WG = LI fMRI BOLD signal in WG; LDT = LI fMRI BOLD signal in lexical decision; DL = LI accuracy dichotic listening; VHFP = LI naming times pictures; VHFW = LI naming times words. *p < .05 in this analysis or in the analysis of participants with data on all measures (n = 34). **p < .05 in both analyses. aA cell where the correlation is circular, for example, the STG and vOT clusters were defined based on fMRI activation in WG, so a high correlation is expected. Greve et al. 1485 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 5 / 9 2 5 1 / 4 9 7 / 7 1 1 4 9 7 4 7 5 / 5 1 6 7 0 7 o 9 c 4 n 2 _ 5 a / _ j 0 o 0 c 4 n 0 5 _ a p _ d 0 0 b 4 y 0 g 5 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j f . t / u s e r o n 1 7 M a y 2 0 2 1 the analyses are limited to those participants for whom we have data on all measures. DISCUSSION This study tested the surprising finding of Keller et al. (2011) that only the volume of the insula differs between healthy LLD and RLD participants (language dominance based on asymmetry in the fMRI BOLD signals during a silent WG task). We made use of the MRI scans collected by Van der Haegen et al. (2011), augmented with 19 scans collected later (footnote 2). In addition, we made use of the FreeSurfer software, which allowed us to run explor- atory analyses with a greater precision. Our results replicate Keller et al. for all regions tested by them. Only the size of the insula differed significantly be- tween LLD and RLD participants. No significant differences were found for Brocaʼs area or the PT, although the left- ward asymmetry tended to be smaller for RLD participants than for LLD participants, which is intuitively more accept- able than the reverse trends reported by Keller et al. We also failed to replicate the rightward insular asymmetry in RLD participants, reported by Keller et al. As for the other brain regions, we found evidence for a reduced left asym- metry. In addition, we observed two new areas with reliable differences between the LLD and RLD groups (showing reversed asymmetries in the two groups). These were STG and vOT, two regions involved in language perception in the spoken and written modalities, respectively. Our data, together with those of Keller et al. (2011), point to a divergence between the anatomical and functional asymmetries in participants with right speech dominance. Although the participants had Brocaʼs area clearly lateralized to the right and we know that most of them also had spoken and written language perception lateralized to the RH (Van der Haegen et al., 2012, 2013), their overall volume asymmetry was leftward (Figure 6). This casts doubts on the hypothesis that the anatomical asymmetry of the human brain is the direct cause of the functional asymmetries observed. This finding also seems to put boundary conditions on the extent to which brain use can modulate GM volume. After all, despite strong functional asymmetries in language ( Van der Haegen et al., 2011, 2012), tool use ( Vingerhoets et al., 2013), and visuospatial processing (Cai et al., 2013), the anatomi- cal differences between both groups were surprisingly small in size and area. In the remainder of the discussion, we give a more de- tailed description of the findings in the different ROIs and we end with some methodological observations, given that this study also introduces various new ways of brain analysis. PT The PT has been claimed to have a central role in spoken language processing. Geschwind and Levitsky (1968), found significant anatomical laterality in PT, and hypothe- sized that this was related to language laterality. Several studies to test this hypothesis followed, many of which found that PT was lateralized to the left (Keller et al., 2007, 2011; Dos Santos Sequeira et al., 2006; Eckert, Leonard, Possing, & Binder, 2006), although not all (Dorsaint-Pierre et al., 2006). Foundas, Leonard, Gilmore, Fennell, and Heilman (1994) found PT asymmetry that was reversed with RLD but only had one RLD participant. Other studies have not been able to find a PT difference between LLD and RLD (Keller et al., 2011; Dorsaint-Pierre et al., 2006), although Tzourio et al. (1998) reported that the area of left PT (rather than LI) was predictive of lan- guage laterality. We also looked for unilateral differences in PT surface area and found none. In fact, we found no unilateral LLD–RLD differences on any of our measures. As with other studies, we found a significant left later- alization to PT in both LLD and RLD. As with Keller et al. (2011) and Dorsaint-Pierre et al. (2006), we did not find an LLD–RLD difference when analyzing the anatomically defined PT. The asymmetry in volume was driven by asymmetry in PT surface area and not in thickness. In the exploratory analysis, however, a small area that partially overlapped with both the PT and LASTG was found in which volume LI was greater for LLD than RLD. A post hoc analysis showed that the LLDs were leftward later- alized (positive LI) whereas the RLDs were rightward later- alized (negative LI). It also showed that LLD > RLD in
surface area LI but not in thickness. The STG is specifically
involved in processing the phonetic/phonological aspects
of spoken language (Turkeltaub & Coslett, 2010; Desai,
Liebenthal, Waldron, & Binder, 2008). It has also been
linked to auditory STM (Richardson et al., 2011). Interest-
ingly, this region does not seem to fit well within Hickok
and Poeppel (2007) model of speech processing, according
to which STG is involved in the spectrotemporal analysis of
auditory input and does so bilaterally. Andererseits,
the STG cluster is close to an area called SPT (sylvian parietal
temporal cortex). Hickok and Poeppel (2007) speculate that
area SPT is involved in translation between sensory speech
codes and the motor system (though this function extends
beyond speech tasks). Given that the functional asymmetry
was based on asymmetric fMRI results of a WG task, es ist
not impossible that the STG cluster overlaps with SPT.
At first glance, the exploratory analysis appears to con-
tradict the ROI results, which did not show a difference in
PT. Jedoch, the PT ROI was trending toward signifi-
Abbruch, and the STG cluster comprised only a small part
of PT. Analyzing the entire PT may wash out this effect.
An advantage of the exploratory technique is that it can
find clusters that do not fall into standard anatomical
definitions. This finding does add some support to the
Geschwind and Levitsky (1968) Hypothese.

The only other research to apply an exploratory
method to the study of LLD versus RLD was Dorsaint-
Pierre et al. (2006). They used VBM in a group of epilepsy
Patienten. Dorsaint-Pierre et al. (2006) did not find any

1486

Zeitschrift für kognitive Neurowissenschaften

Volumen 25, Nummer 9

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LLD–RLD differences in the PT/STG area (although they
did find that simple laterality was significantly positive).
This could be for several reasons. Erste, they only had
11 RLD participants. Zweite, they used a sample of epi-
lepsy patients who may have reorganized language areas
(Brazdil, Zakopcan, Kuba, Fanfrdlova, & Rektor, 2003).
Endlich, it may be that VBM is less sensitive than surface-
based techniques to these types of effects.

Josse et al. (2009) performed a similar VBM analysis.
Jedoch, instead of classifying participants as LLD or
RLD, they performed a voxel-by-voxel regression analysis
between GM concentration asymmetry and fMRI activa-
tion asymmetry and found widespread correlation, In-
cluding in the PT/STG region. Although this supports
our results, there are several potential differences with
unsere Arbeit. Josse et al. (2009) report having 12 RLD par-
ticipants; Jedoch, by our definition of RLD (d.h., LI < −.6), they would have had only four RLD. Therefore, it is probably the case that our RLD participants are signifi- cantly more right lateralized for language. In addition, they found large correlations between GM density and functional activation across the brain even for non- language tasks. This suggests that the correlations may have been an artifact possibly caused by partial voluming, that is, if there is more GM in a voxel, then one would expect more functional activation in that voxel. POp and PTr The POp and PTr comprise Brocaʼs area, which is related to speech production.4 Studies by Keller et al. (2007, 2011) both found that POp was left lateralized but that PTr was not. Keller et al. (2011) did not find an LLD– RLD difference in either POp or PTr. Our anatomically defined ROI results are in complete agreement as are the exploratory results despite the fact that Keller et al. (2007, 2011) employed manual labeling and used only right-handed participants. We also found that the POp laterality was driven by left–right differences in surface area, not thickness. Dorsaint-Pierre et al. (2006), how- ever, did find an LLD–RLD difference in GM concentra- tion in Brocaʼs area using (exploratory) VBM. The use of an epilepsy patient population may contribute to this discrepancy. Dorsaint-Pierre et al. (2006) also comment that their findings may be because of differences in cur- vature of the cortex that can occur in certain types of epilepsy and not because of change in GM volume. If so, then the differences may not have shown up in this surface-based because we only analyzed volume, area, and thickness. Insula Keller et al., 2011). The numerical difference between LIs for LLD and RLD was actually smaller for insula as com- pared with PT. The significance came from smaller vari- ability, which may be because of the fact that the insula is a much larger structure and so its total volume will not be very sensitive to small changes in its boundaries or perhaps could be because of the fact that the circular sulcus and the gyri of the insula are more easily identi- fied. In Keller et al. (2011), the variance of insula was also much less than for other structures, but they also found a large difference in laterality. In this study, both LLD and RLD had significant leftward asymmetries. Kel- ler et al. (2011) showed a positive LI for LLD, but a ne- gative LI for RLD. The reason for this is unclear but perhaps related to the use of left-handers in this study and right-handers in Keller et al. (2011). Interestingly, there was not a cluster in insula in the exploratory ana- lysis. Further investigation showed a consistent but small LLD > RLD advantage across insula in the explora-
Tory-Analyse. It appears that this advantage is not strong
enough on a vertex-by-vertex basis to create a cluster,
but it is strong enough when averaged over the entire
insula. Keller et al. (2011) came to a similar conclusion
when they explored the spatial pattern of LLD/RLD dif-
ferences in insula.

Given our replication of Keller et al., we fully agree
with those authors that the role of the insula in language
processing needs further investigation, also given the
correlations with DL performance (Tisch 3). The insula
seems to be particularly involved in overt speech,
although there is also some evidence for a role in covert
Rede (Price, 2012), and insular lesions are related to
aphasia (Price, 2000; Donnan, Darby, & Saling, 1997).
Andererseits, the insula plays a role in a wide range
of intermodal functions (Flynn, Benson, & Ardila, 1999),
making its involvement in language processing less pro-
nounced than other peri-sylvian areas. Future research is
needed to investigate whether the central location within
the peri-sylvian network makes the insula one of the key
regions to determine language asymmetry.

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HG, primary auditory cortex, has been studied in relation
to language laterality (Dorsaint-Pierre et al., 2006). Dort
was not a significant difference in HG anatomical asym-
metry between LLD and RLD in agreement with manual
labelings in Dorsaint-Pierre et al. (2006). We did find that
HG was significantly lateralized to the left, in agreement
with other studies (Dorsaint-Pierre et al., 2006; Penhune
et al., 1996, 2003; Rademacher et al., 1993).

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There was a significant difference between LLD and RLD
in the volume asymmetry of insula (in agreement with
Keller et al., 2011). This difference was driven by area
asymmetries and not thickness (again in agreement with

vOT Cortex

The only other area to be significantly different be-
tween LLD and RLD was in the vOT with LLDs being more

Greve et al.

1487

left-lateralized and RLDs more right-lateralized in GM
Volumen. Dorsaint-Pierre et al. (2006) did not find an
LLD–RLD difference in vOT. To our knowledge, this is
the first report of anatomical asymmetry differences
between LLD and RLD in vOT. This area has been linked
to visual word recognition (Dehaene & Cohen, 2011;
Ben-Shachar, Dougherty, Deutsch, & Wandell, 2007;
Cohen et al., 2000), face recognition (Kanwisher, McDermott,
& Chun, 1997), and body and object recognition (Taylor &
Downing, 2011). The asymmetries observed are in line
with the functional lateralization of visual word recognition
(Van der Haegen et al., 2012; Seghier & Price, 2011; Cai
et al., 2008, 2010). Reduced GM volume in the left or bi-
lateral vOT regions has been found to be associated with
dyslexia (Raschle, Chang, & Gaab, 2011). The face recogni-
tion area (d.h., the fusiform face area) is also known to be
functionally lateralized but in the opposite direction rela-
tive to the visual word recognition area. Jedoch, we do
not see an area in Figure 4 where RLD > LLD inside of
vOT. A possible reason for this is that all the participants
in this study were left-handers and the FFA is less later-
alized in left-handers than right-handers (Willems, Peeling,
& Hagoort, 2010). Another reason might be that FFA and
the visual word recognition area are complementary (Plaut
& Behrmann, 2011; Dien, 2009) and cannot be separated
anatomically.

It does not come as a surprise that asymmetry in the
WG task predicts anatomical asymmetry in vOT. Beweis
suggests that phonological information contributes to
visual word recognition (Price & Devlin, 2011). Hemi-
sphere dominance for word reading tends to follow that
of speech production (Van der Haegen et al., 2012; Cai
et al., 2008, 2010), perhaps reflecting the need for fast
interactions between these areas. Auch, see below for cor-
relations between vOT and the LDT. In the history of the
human species, reading is a very recent skill with no real
evolutionary pressure for laterality yet. The fact that it is
lateralized is arguably because of the close interactions
with Brocaʼs area.

Bilateral Participants

Eleven participants did not show strong language lateral-
ität. In the anatomically defined ROI analysis, they showed
a similar laterality pattern as the LLD and RLD partici-
Hose, but there was no systematic relationship. Im
exploratory and confirmatory analysis of the STG cluster,
the LI for volume, Bereich, and thickness consistently fell
between LLD and RLD.

Correlations between fMRI and
Behavioral Measures

In line with the absence of a clear reversal of the overall
anatomical asymmetry in RLD participants (Figur 1),
most of the LIs in the anatomically defined ROIs were

uncorrelated with functional asymmetries. This was true
both for asymmetries based on fMRI BOLD signals ( WG
and LDT) and on behavioral data (DL, VHFP, and VHFW).
Only insula showed correlations with the functional
asymmetries (DL and WG). The STG cluster correlated
with VHFP but, überraschenderweise, not with DL. The anatomical
asymmetries in the vOT cluster correlated with DL and
LDT, in line with the proposal that this area plays a
central role in the integration of phonological and ortho-
graphic word information as mentioned above. The find-
ing that anatomical vOT asymmetries are correlated with
the ear advantage in DL is particularly noteworthy, wie es ist
in line with the hypothesis that the orthography of a
word helps the processing of spoken words in literate
individuals (Ziegler & Ferrand, 1988). It is surprising vOT
is not correlated with the VHF differences in word naming
and picture naming.

Statistical Model Selection

The statistical analysis requires a model of the relation-
ship between the functional LI (fLI) and structural LI
(sLI). One of two models is typically used: (1) a cate-
gorical model in which participants are divided into lateral-
ity classes; differences in class means are evidence of an
interaction between structure and function, oder (2) A
continuous model in which it is assumed that fLI and
sLI are linearly related; a nonzero slope is evidence of
an interaction. There are advantages and disadvantages
to both methods. Categorization models the fLI–sLI rela-
tionship as piece-wise constant. Some researchers (z.B.,
Keller et al., 2011) use two categories (LLD and RLD),
Andere (z.B., Dorsaint-Pierre et al., 2006) use three
(LLD, RLD, and BLD). We chose three for several reasons.
Erste, bilateral participants may represent a qualitatively
different language organization than the mirror reversal
hypothesized between LLD and RLD. Zum Beispiel, in einem
Wada test, bilaterality may manifest as both hemispheres
being impaired or neither being impaired (Dorsaint-Pierre
et al., 2006; Risse, Gates, & Fangman, 1997). Zweite,
having three classes makes the piece-wise constant model
more accurate in the case where there is continuous
Variation. We chose a relatively high threshold of 0.6 Zu
assure that our results were not influenced by bilateral
Teilnehmer (d.h., we would rather exclude a few true LLDs
or RLDs rather than risk the inclusion of BLDs).

A continuous model is attractive because language lat-
erality is never an “all-or-nothing” trait. The continuous
model only requires one regressor, saving several DOFs
relative to the categorical model—a very important point
when sample sizes are small. The ability to include bi-
lateral participants also increases the DOF, Aber, as men-
tioned above, it is not clear that the bilateral participants
belong with the others. The continuous model requires a
parametric function relating fLI and sLI (generally linear).
This is a very strong assumption given that the sLI and fLI
are computed from very different data and methods. In einem

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voxelwise structural analysis, this relationship could
change from region to region.

Daher, the categorical model makes fewer assumptions
than the continuous model but may have less power. Der
exact nature of the fLI–sLI relationship is complex and
will require much more data to sort out. For complete-
ness, we performed a follow-up test using a continuous
linear model. Although the exact p values changed, Dort
were no changes in the conclusions made from either the
ROI or the exploratory analysis (d.h., the STG and vOT
clusters and insula ROI were still significant and no new
areas became significant).

Methodological Considerations

This study exploited some unique methodologies. Erste,
we had a relatively large sample (n = 21) of healthy RLD
participants made possible by a low-cost and noninvasive
screening procedure. The sample was mainly selected
after screening 250 left-handers for behavioral signs of
RLD. Our sample is twice as large as comparable studies
(n = 10, Keller et al., 2011; n = 11, Dorsaint-Pierre et al.,
2006; n = 4, Eckert et al., 2006). One disadvantage of the
study is that the participants are only left-handers and
mostly women. This prevented us from exploring the
relationship of handedness and gender on the interaction
between language and anatomical laterality.

We employed automated surface-based methods to
compute anatomical measures. These methods include
3-D mesh models with subvoxel accuracy; surface-
based registration that aligns folding patterns across sub-
ject and hemisphere; and automated algorithms that
attempt to replicate the manual labeling of an anatomist.
The volumes of the anatomically defined ROIs were in
the range of published manual results but consistently
smaller. Zum Beispiel, we measured the left PT volume
(including PP) in LLD to be 2204 mm3 whereas other
Studien (Keller et al., 2007, 2011; Dos Santos Sequeira
et al., 2006) that employed manual labelings were in
the range of 3000–4000 mm3. A possible limitation of
the PT results is that the Destrieux definition of PT
included the PP, which is normally excluded. This may
have the effect of reducing asymmetry. In der Tat, the PT
LIs in this study (.09–.14) are much less than that of
Dos Santos Sequeira et al. (2006). Jedoch, they are
more than the PT LIs in Keller et al. (2007) (um .11)
and about the same as in Keller et al. (2011) (.09–.14).
The POp and PTr volumes in this study are about 66%
of that found in Keller et al. (2007). Although some of
this may have to do with the landmark definitions, it may
also be because of partial voluming. Voxels that are partially
in GM and WM will have an intensity that is darker than the
typical WM voxel, and a manual rater may be inclined to
label these as GM. The surface-based mesh model will
cut through the middle of such voxels and thereby give a
more accurate (and smaller) measure of GM volume.
Despite this difference, the ratio of LH to RH PT volume

in this study (1.3) is very close to that found in Dos Santos
Sequeira et al. (2006) (1.4), Keller et al. (2007) (1.2), Und
Keller et al. (2011) (1.17). For POp, the simple volume LI
was very similar for POp across the studies (0.12 for this
Studie, 0.11 for Keller et al., 2011, and about 0.13 for Keller
et al., 2007). There is a striking similarity between the ROI
results presented here and those of Keller et al. (2011)
despite appreciable differences in methodology. Obwohl
the ROI results are similar to previous work, we point out
that the exploratory results are novel.

The surface-based registration allowed an exploratory
analysis to search for areas of significant LLD/RLD differ-
ence that do not lie cleanly within the boundaries pre-
scribed by manual labels. Such a difference was found in
a cluster whose extent covered only part of the PT. Der
exploratory effect did not show up in the ROI approach
because this cluster did not fit neatly into an anatomically
defined region. The exploratory approach also allows
evaluating brain regions that are outside of traditional
language areas and so not targeted for tedious manual
labeling procedures. Such a region was found in vOT.

The interhemispheric registration procedure was veri-
fied in two ways. Erste, the entire study was replicated
using the RH and LH as initial targets, with very similar
results. Zweite, an ROI covering the exploratory STG
cluster was defined based on anatomical landmarks
(and so independent of the registration). This ROI
showed significant differences between LLD and RLD.
Although this by itself does not confirm a difference be-
tween LLD and RLD, it does show that the difference
found in the exploratory analysis is not a byproduct or
artifact of the registration method. The exploratory surface-
based analysis is different from VBM analysis in several
ways. It uses surface-based registration, which has been
shown to be superior to volume-based in many instances
(Tucholka, Fritsch, Polina, & Thirion, 2012; Zollei, Stevens,
Huber, Kakunoori, & Fischl, 2010; Postelnicu, Zollei, &
Fischl, 2009; Fischl et al., 2008). The surface-based method
allows the differences in GM volume to be decomposed
into effects of surface area and thickness. This is important
for shedding light on the origins of asymmetry. For exam-
Bitte, surface area and thickness are both highly heritable but
genetically independent (Winkler et al., 2010; Panizzon
et al., 2009). Separately measuring surface area and thick-
ness can also be important in understanding disorders. Für
Beispiel, dyslexia and reading skills have been found to be
related to surface area but not to thickness (Frye et al.,
2010). GM volume loss in aging is related to loss of surface
area not cortical thinning; Jedoch, cortical thinning
accompanies Alzheimer disease (Dickerson et al., 2009).
Andererseits, VBM may be sensitive to less specific
aspects of morphometry (Palaniyappan & Liddle, 2012).

Abschluss

The purpose of this study was to investigate the inter-
action between functional language laterality and anatomical

Greve et al.

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laterality. There were several novel aspects to the study
including the recruitment of a relatively large healthy sample
of RLD participants made possible by a previously devel-
oped low-cost and noninvasive screening procedure. Das
study also used automated surface-based analysis tech-
niques that provide several useful features: the ability to
automatically delineate standard language-related anatomi-
cal ROIs such as PT, POp, and PTr; the ability to compute
GM volume with subvoxel accuracy; the ability to decom-
pose differences in GM volume into differences in surface
area and thickness; and the ability to use surface-based
registration to align folding patterns across hemisphere as
well as participant for use in an exploratory analysis. Der
anatomical ROI-based results replicated those of a similar
study using manual labeling in healthy right-handers (Keller
et al., 2011) in that no LLD–RLD differences were found in
PT, POp, and PTr, but a strong difference was found in insula
GM volume. Interessant, the exploratory analysis did reveal
a small LLD > RLD cluster in STG that overlapped with PT.
This capability shows the utility of exploratory analysis for
finding structural differences that may cross the boundaries
of anatomically defined ROIs. The exploratory analysis also
indicated an LLD–RLD difference in vOT, an area associated
with visual word recognition. No other differences between
LLD and RLD were found in the rest of the brain, including in
Brocaʼs area. In all cases, differences in GM volume were
attributable to a difference in cortical surface area and not
cortical thickness. The sample used in this study was exclu-
sively left-handed and mostly female, making it impossible
to evaluate the effect of handedness and gender; Jedoch,
the use of a practical language dominance screening proce-
dure and automated surface-based analysis should make
more large-scale studies of language dominance possible.

Danksagungen
Support for this research was provided in part by the National
Institutes of Health (R01NS052585-03), the National Institute on
Altern (5R01AG029411, 5R01AG022381-08), the National Institute
of Neurological Disorders and Stroke (1R01NS069696-01A1), Die
National Center for Research Resources (P41-RR14075, R01
RR16594-01A1, the NCRR BIRN Morphometric Project BIRN002,
and Functional Imaging Biomedical Informatics Research Network
[FBIRN] U24 RR021382), and the National Institute for Biomedical
Imaging and Bioengineering (R01 EB001550, R01 EB006758, 1K25
EB013649-01). The scanning was made possible by an Odysseus
Grant paid by the Government of Flanders to M. B.

Reprint requests should be sent to Douglas N. Greve, Der
Martinos Center for Biomedical Imaging, Department of
Radiology, Massachusetts General Hospital, Room 2301, 149
13th Street, Charlestown, MA 02129, oder per E-Mail: greve@nmr.
mgh.harvard.edu.

Notes

It was also found that participants with right hemisphere
1.
speech lateralization had an atypical
left-ear advantage in a
dichotic listening task (Van der Haegen et al., 2013), vorschlagen
that the brain areas involved in spoken word recognition are

atypically lateralized as well, but no fMRI data have been col-
lected on this yet.
2. We tested 19 participants more after the Van der Haegen
et al. (2011) manuscript was accepted for publication. Most of
these additional participants were not previously screened with
the VHF tasks.
3. As can be seen in the Results section, this turned out to be
the case for about half of the participants.
4. Modern conceptions of Brocaʼs area are more complex
than simply a “motor speech” area, Zum Beispiel, Carreiras,
Pattamadilok, Meseguer, Barber, and Devlin (2012) have found
evidence that Brocaʼs area is involved in the understanding of
syntax.

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