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Right hemisphere grey matter structure and
language outcomes in chronic left hemisphere
stroke
Shihui Xing,
1,2
Elizabeth H. Lacey,
1,3
Laura M. Skipper-Kallal,
1
Xiong Jiang,
4
Michelle L. Harris-Love,
3,5
Jinsheng Zeng
2
and Peter E. Turkeltaub
1,3
The neural mechanisms underlying recovery of language after left hemisphere stroke remain elusive. Although older evidence
suggested that right hemisphere language homologues compensate for damage in left hemisphere language areas, the current
prevailing theory suggests that right hemisphere engagement is ineffective or even maladaptive. Using a novel combination of
support vector regression-based lesion-symptom mapping and voxel-based morphometry, we aimed to determine whether local
grey matter volume in the right hemisphere independently contributes to aphasia outcomes after chronic left hemisphere stroke.
Thirty-two left hemisphere stroke survivors with aphasia underwent language assessment with the Western Aphasia Battery-
Revised and tests of other cognitive domains. High-resolution T
1
-weighted images were obtained in aphasia patients and 30
demographically matched healthy controls. Support vector regression-based multivariate lesion-symptom mapping was used to
identify critical language areas in the left hemisphere and then to quantify each stroke survivor’s lesion burden in these areas. After
controlling for these direct effects of the stroke on language, voxel-based morphometry was then used to determine whether local
grey matter volumes in the right hemisphere explained additional variance in language outcomes. In brain areas in which grey
matter volumes related to language outcomes, we then compared grey matter volumes in patients and healthy controls to assess
post-stroke plasticity. Lesion–symptom mapping showed that specific left hemisphere regions related to different language abilities.
After controlling for lesion burden in these areas, lesion size, and demographic factors, grey matter volumes in parts of the right
temporoparietal cortex positively related to spontaneous speech, naming, and repetition scores. Examining whether domain general
cognitive functions might explain these relationships, partial correlations demonstrated that grey matter volumes in these clusters
related to verbal working memory capacity, but not other cognitive functions. Further, grey matter volumes in these areas were
greater in stroke survivors than healthy control subjects. To confirm this result, 10 chronic left hemisphere stroke survivors with no
history of aphasia were identified. Grey matter volumes in right temporoparietal clusters were greater in stroke survivors with
aphasia compared to those without history of aphasia. These findings suggest that the grey matter structure of right hemisphere
posterior dorsal stream language homologues independently contributes to language production abilities in chronic left hemisphere
stroke, and that these areas may undergo hypertrophy after a stroke causing aphasia.
1 Department of Neurology, Georgetown University Medical Center, Washington, D.C., USA
2 Department of Neurology, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
3 Research Division, MedStar National Rehabilitation Hospital, Washington, D.C., USA
4 Department of Neuroscience, Georgetown University Medical Center, Washington, D.C., USA
5 Department of Rehabilitation Science, George Mason University, Fairfax, V.A., USA
Correspondence to: Peter E. Turkeltaub MD, PhD,
Department of Neurology,
Georgetown University Medical Center,
Research Division,
doi:10.1093/brain/awv323 BRAIN 2016: 139; 227–241 |227
Received June 12, 2015. Revised August 15, 2015. Accepted September 23, 2015. Advance Access publication October 31, 2015
ßThe Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
For Permissions, please email: journals.permissions@oup.com
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MedStar National Rehabilitation Hospital,
4000 Reservoir Rd, NW, Building D, Suite 165,
Washington D.C., 20057,
USA
E-mail: turkeltp@georgetown.edu
Keywords: aphasia; stroke; grey matter; outcome; right hemisphere; voxel-based morphometry
Abbreviations: AQ = aphasia quotient; PCAD = proportion of critical area damaged; SVR-LSM = support vector regression-based
lesion–symptom mapping; VBM = voxel-based morphometry; WAB-R = Western Aphasia Battery-Revised
Introduction
Approximately one-third of stroke survivors have aphasia,
a loss of language ability, as one of their presenting symp-
toms. Two-thirds of these individuals never regain their
prior level of language functioning (Gottesman and Hillis,
2010). Thus, 20% of stroke survivors are left with
chronic aphasia, which causes difficulty communicating in
daily life, decreases quality of life and causes substantial
long-term disability (Gottesman and Hillis, 2010; Berthier
and Pulvermuller, 2011; Gialanella et al., 2011; Gonzalez-
Fernandez et al., 2013).
Engagement of preserved left hemisphere language areas
and recruitment of nearby perilesional tissue is thought to
support aphasia recovery after stroke (Karbe et al.,1995;
Heiss et al., 2003; Saur et al., 2006; Winhuisen et al.,
2007; Meinzer and Breitenstein, 2008; Martin et al.,2009;
Fridriksson, 2010; Fridriksson et al., 2012). The role of the
right hemisphere in aphasia recovery has been debated since
the late 19th century and remains controversial. Evidence of
right hemisphere involvement in aphasia recovery begins
with Barlow’s 1877 case of a boy who recovered from apha-
sia after a left inferior frontal gyrus stroke and later wor-
sened again after another stroke to the same location in the
right hemisphere (Barlow, 1877). In addition to more recent
cases of sequential left and right hemisphere strokes in adults
(Basso et al., 1989; Turkeltaub et al., 2012), other lines of
evidence suggest right hemisphere compensation in aphasia:
a relationship between poor aphasia outcomes and ‘clinically
silent’ right hemisphere strokes (Yarnell et al., 1976), wor-
sening of language in aphasic patients after right carotid
anaesthesia (Kinsbourne, 1971), and left visual field and
left ear advantages in people with aphasia (Moore and
Weidner, 1974, 1975; Johnson et al., 1977; Moore and
Papanicolaou, 1988). These sources lack the spatial reso-
lution to implicate specific parts of the right hemisphere in
aphasia recovery, but suggest that overall the right hemi-
sphere compensates for language deficits after damage to
thenativelefthemispherenetwork.
However, recent transcranial magnetic stimulation stu-
dies of aphasia have shown that inhibiting the right inferior
frontal gyrus improves language functions (Naeser et al.,
2005; Martin et al., 2009; Barwood et al., 2011;
Hamilton et al., 2011). These studies seemingly suggest
that right hemisphere recruitment may have a negative
impact on aphasia outcome. However, most brain
stimulation studies aimed at suppressing right hemisphere
processing in chronic aphasia have used protocols thought
to inhibit the targeted area immediately after stimulation
(Hallett, 2000; Zaghi et al., 2010). There is scarce evidence
that the language benefits of these techniques in chronic
aphasia are related to long-term right hemisphere inhib-
ition. Moreover, the effects of right hemisphere inhibition
in areas outside the right inferior frontal gyrus have not
been thoroughly investigated.
Functional imaging findings have also raised questions
about whether right hemisphere recruitment may be ineffect-
ive or even maladaptive in aphasia recovery (Belin et al.,
1996; Cao et al., 1999; Winhuisen et al., 2005; Postman-
Caucheteux et al., 2010; Allendorfer et al., 2012). In longi-
tudinal studies, right hemisphere recruitment has often
peaked early in recovery and has diminished over time in
association with clinical improvements (Fernandez et al.,
2004; Saur et al., 2006; Kurland et al., 2008; Breier et al.,
2009), seemingly suggesting that disengaging the right hemi-
sphere is beneficial for long-term aphasia recovery.
There is thus a great deal of inconsistency in the litera-
ture on right hemisphere contributions to aphasia outcome.
This inconsistency may arise in part because of individual
differences in recovery. Patients with small left hemisphere
lesions and less severe aphasia may be able to recruit resi-
dual left hemisphere language-capable areas (Heiss et al.,
1999), and thus have little right hemisphere activity. In
contrast, individuals with large left hemisphere lesions
have more severe aphasia because of the extensive
damage to the native left hemisphere language network,
and must rely on the right hemisphere more (Anglade
et al., 2014). This relationship may lead to the erroneous
impression that greater right hemisphere engagement causes
worse outcomes, when in fact the causal relationship is
reversed. To reveal the true contributions of the right hemi-
sphere to language recovery, it is necessary to first estimate
the likely severity of aphasia given the features of the indi-
vidual and his/her stroke. Only then can one assess whether
right hemisphere engagement alters this outcome, either
positively or negatively.
Additionally, the impact of performance and effort on
task-related activity further complicates the interpretation
of functional imaging results. While inverse correlations
between activity and performance may suggest maladaptive
activity, an alternate interpretation is that individuals with
more severe aphasia must exert more effort to perform the
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task and thus engage the right hemisphere to a greater
degree (Just et al., 1996; Fridriksson and Morrow, 2005).
Therefore, in addition to task-related functional imaging,
measures of the right hemisphere that are independent of
effort or difficulty should be examined. Indeed, some recent
diffusion tensor imaging studies have suggested that integ-
rity of right hemisphere white matter pathways relates posi-
tively to aphasia outcomes (Schlaug et al., 2010; Forkel
et al., 2014).
In the present study, we addressed whether language out-
comes in chronic aphasia relate to grey matter volume in
brain areas not directly impacted by stroke. As noted
above, to measure the contributions of the right hemi-
sphere, we first needed to predict the likely language out-
comes for each individual based on his/her pattern of left
hemisphere damage. To accomplish this, we used support
vector regression-based multivariate lesion-symptom map-
ping (SVR-LSM) to identify critical left hemisphere areas
for different language functions, and then calculated the
amount of damage in these areas suffered by each stroke
survivor. We then used these values along with total lesion
size and demographic factors to predict the likely language
outcome for each individual based on the direct effects of
his/her stroke. Next, we used voxel-based morphometry
(VBM) to examine whether local grey matter volume of
the uninjured brain tissue contributed to language out-
comes independent of these stroke and demographic fac-
tors. Finally, for each area in which grey matter volume
related to language outcomes, we compared the grey
matter volumes in stroke survivors to matched controls to
test for differences potentially reflecting plastic changes in
grey matter related to post-stroke aphasia.
Materials and methods
Participants
The study was approved by the Georgetown University
Institutional Review Board and written informed consent was
obtained from all study participants prior to enrolment in the
study.
Patients
Thirty-two chronic left hemisphere stroke survivors with his-
tory of aphasia were recruited with inclusion criteria as fol-
lows: native English speaker; at least 6 months post-stroke;
able to follow testing instructions; no history of other signifi-
cant neurological illnesses. See Table 1 for characteristics of
the group, and Supplementary Table 1 for the characteristics
of individual participants. All patients had aphasia at the time
of stroke based on medical records and received speech-
language therapy. Most of the patients received several differ-
ent types of speech-language therapy from multiple clinicians
targeting various aspects of speech and language at different
times in their recovery. Many also used tablet or smartphone
apps to augment speech therapy. Thus, the type and total dose
of therapy are not easily quantifiable.
Healthy control subjects
Thirty healthy control subjects without neurological or psychiatric
disorder, and matched to the stroke group on age, education,
handedness, and gender, were enrolled in the study (Table 1).
Aphasia and cognitive testing
Patients were given the Western Aphasia Battery-Revised
(WAB-R) (Kertesz et al., 1982), which includes subtests that
provide composite scores of Spontaneous Speech, Repetition,
Naming/Word-Finding and Auditory-Verbal Comprehension.
Totalling these scores provides the Aphasia Quotient (AQ), a
measure of overall aphasia severity on a scale of 0–100.
Other language and cognitive domains were tested with the
Cognitive Linguistic Quick Test executive composite, excluding
generative naming to provide a pure non-language measure;
forward and backward digit span (Wechsler, 1987), with an
option to respond by pointing to a number line to prevent
motor speech deficits from limiting performance; Corsi
Blocks forward and backward spatial span (Corsi, 1972);
Apraxia battery for adults-2 subtest 2A (Dabul, 2000) and a
30-item pseudoword repetition task.
Image acquisition
MRI data were acquired on a 3.0 T Siemens Trio scanner at the
Georgetown University Medical Center with a 3D T
1
-weighted
sequence (magnetization-prepared rapid-acquisition gradient
echo) with the following parameters: repetition time = 1900ms;
echo time = 2.56 ms; flip angle = 9; 160 contiguous 1 mm sagittal
slices; field of view = 250 250 mm; matrix size = 246 256,
voxel size = 1 11 mm; slice thickness = 1 mm.
Image data preprocessing
Support vector regression-based lesion-symptom
mapping protocol
SVR-LSM, a multivariate lesion-symptom mapping approach
(Zhang et al., 2014), was used to define critical left hemisphere
areas in which damage relates to language impairment on a
given WAB-R measure. These results were used to quantify in-
dividual differences in left hemisphere stroke locations that
relate to the behavioural scores, which is vital to determine if
grey matter volume in the right hemisphere contributes add-
itional variance to scores. Lesion masks were created by manu-
ally tracing stroke damage on the T
1
-weighted images in native
space in MRIcron (Rorden and Brett, 2000). All lesion masks
were checked by two board certified neurologists (S.X. and
P.E.T.) and then warped into the Montreal Neurological
Institute (MNI) space by applying deformation fields derived
from the VBM8 analysis (see below). A lesion overlap map is
shown in Fig. 1. Multivariate lesion-symptom mapping is more
resistant than voxel-based lesion–symptom mapping to localiza-
tion errors due to lesion covariance and contributions of mul-
tiple brain regions to a given behaviour (Mah et al.,2014;
Herbet et al., 2015). Here, multivariate lesion–symptom map-
ping was carried out using SVR-LSM running under Matlab
R2014a (Zhang et al., 2014). SVR-LSM uses a machine learn-
ing-based multivariate support vector regression algorithm to
find lesion–symptom relationships. SVR-LSM analyses were
conducted for the overall severity (WAB-R AQ) as well as for
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each WAB-R subscore (Spontaneous Speech, Naming/Word-
Finding, Repetition and Auditory-Verbal Comprehension).
Only voxels damaged in at least 10% of patients were included
in the analysis. Probabilistic maps created using 10 000 permu-
tations of the behavioural scores were thresholded at P50.001
to define the critical areas of the left hemisphere in which
damage causes deficits on a given WAB-R subscore. The
Proportion of Critical Area Damaged (PCAD) was then calcu-
lated for each patient for each WAB-R score.
Voxel-based morphometry protocol
VBM is a whole-brain technique designed to discover subtle,
local changes in grey matter density or volume by applying
voxel-wise statistics within the context of Gaussian random
fields (Ashburner and Friston, 2000). The structural volume
preprocessing and analysis were performed using the VBM8
toolbox in Statistical Parametric Mapping software (SPM8;
http://www.fil.ion.ucl.ac.uk/spm) running under Matlab
R2014a. Prior to preprocessing, all images were manually re-
aligned to the anterior commissure to reduce between-subject
variability and lesion tracings were used to mask out damaged
tissue to achieve accurate segmentation and spatial normaliza-
tion. Data were subsequently processed via a procedure of joint
spatial normalization and segmentation using the unified seg-
mentation approach (Ashburner and Friston, 2005).
Specifically, images were corrected for bias-field inhomogeneity,
and segmented into grey matter, white matter, and CSF maps.
The segmented maps were then registered to a standard
template in MNI space using 12-parameter affine linear and
non-linear warping transformation. The segmentation procedure
was refined with a hidden Markov random field model. Grey
matter voxel values were multiplied by the Jacobian matrix
parameters derived from normalization to preserve actual grey
matter values locally (modulated grey matter volumes). The
modulated grey matter volumes were then smoothed with a
Gaussian kernel of 8 mm full-width at half-maximum to
reduce anatomical variability. All voxels were thresholded at
0.2 to avoid possible edge effects between different tissue types.
Statistical analysis
To determine associations between grey matter volume and
behavioural measures, volumetric grey matter images were
submitted to separate multiple regression analyses with the
overall aphasia severity and WAB-R subscores entered as an
explanatory factor. We included factors as nuisance variables
that could relate to behavioural performance or grey matter
volume: age, gender, level of education, handedness, lesion
size, PCAD, and total volume of grey and white matter. We
maintained a cluster-level corrected P50.05 significance
threshold by applying height and extent thresholds of
P50.005 and k= 323, as empirically determined by Monte
Carlo simulation (permutations = 5000, with grey matter
mask) (Gianaros et al., 2008; Schwartz et al., 2010).
To clarify how the nuisance covariates included in the VBM
analysis contributed to language outcomes with and without
Table 1 Demographic data and language performance tests of aphasic patients and healthy controls
Patients group
(n= 32)
Controls group
(n= 30)
Statistics P-
value
Demographic variable
Age (years) 59.13 (9.41) 60.15 (13.90) t(60) = 0.34 0.74
Gender (M/F) 22/10 18/12
2
(1) = 0.52 0.47
Education (years) 16.81 (2.90) 16.57 (2.54) t(60) = 0.35 0.73
Handedness (R/L) 26/6 27/3
2
(1) = 0.96 0.33
Time post stroke (months) 45.46 (35.16) - - -
Lesion size (ml) 116.48 (80.22) - - -
Language evaluation
Aphasia Quotient 68.27 (24.12) - - -
Naming/Word-Finding 6.21 (2.88) - - -
Auditory-Verbal Comprehension 7.49 (1.68) - - -
Repetition 6.39 (2.74) - - -
Spontaneous Speech 13.56 (5.76) - - -
Standard deviations are presented in parenthesis. M = male; F = female; R = right-handed; L = left-handed.
Figure 1 Lesion overlap map of 32 patients with chronic post-stroke aphasia. The n-value denotes the number of patients with a
lesion in each voxel (maximum 23 out of 32). L = left.
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grey matter volume in the model, the variables were further
introduced into a hierarchical linear regression with language
measures as the dependent variables. Partial correlation ana-
lyses were performed to test the relationships between grey
matter volume in right hemisphere clusters identified in the
VBM analysis and the cognitive measures. Further separate
univariate analyses were performed to address group differ-
ences in mean grey matter volume of each identified cluster
between aphasia patients and control subjects. These analyses
were conducted using SPSS version 22.
Results
Demographic and behavioural results
Participant demographics and WAB-R scores are shown in
Table 1. There were no significant differences in age,
gender, level of education or handedness between patient
and healthy control groups. Values for individual patients
are shown in Supplementary Table 1.
Identification of critical left hemi-
sphere areas for language
First, relationships between lesion location in the left hemi-
sphere and language scores on the WAB-R were examined
using SVR-LSM. Different, but partially overlapping lesion
locations were associated with overall aphasia severity
(WAB-R AQ) and each of the WAB-R subscores (Fig. 2A–
E). In order to examine the contribution of grey matter
volume in the spared brain areas to the WAB-R scores, we
first needed to estimate the severity of deficits expected for
each individual based on the lesion itself. To accomplish this,
we used thresholded SVR-LSM maps for the WAB-R scores
as volumes of interest and calculated the proportion of each
volume damaged in each subject (termed PCAD). PCADs
accounted for a large portion of the variance in all WAB-
Rscores(AQ:R
2
=0.62, P=1.010
7
; Naming/Word-
Finding R
2
=0.53, P=2.510
6
; Auditory-Verbal
Comprehension R
2
=0.69, P=5.210
9
;Repetition
R
2
=0.62, P=8.810
8
; and Spontaneous Speech
R
2
=0.62, P=8.610
8
). These strong relationships dem-
onstrate the importance of accounting for the direct effects
of the stroke itself on outcomes before considering whether
undamaged portions of the brain exert additional influence
on outcomes.
Relationship between grey matter
volume and aphasia outcome
To examine whether grey matter volume in areas not dir-
ectly impacted by stroke influence language outcomes, we
performed separate VBM analyses to identify areas of
spared cortex related to overall aphasia severity and
WAB-R subscores. Critically, to account for interindividual
differences that might relate to aphasia outcomes or grey
matter volume, we included a number of nuisance covari-
ates in the VBM analyses: PCAD, lesion size, total volumes
of grey matter and white matter, age, gender, education
and handedness. The VBM analysis on overall aphasia
severity (WAB-R AQ) demonstrated a positive relationship
between grey matter volume and AQ in the right tempor-
oparietal cortex including the supramarginal gyrus and pos-
terior superior temporal gyrus, as well as in the left
cerebellum lobules IV–VI (Fig. 3A and Table 2). For
Repetition, a positive relationship between grey matter
volume and performance was observed in the right tempor-
oparietal cortex including the supramarginal gyrus, the pos-
terior superior temporal gyrus and the posterior middle
temporal gyrus (Fig. 4A and Table 2). For Naming/Word-
Finding, grey matter volume in the right supramarginal
gyrus and the posterior superior temporal gyrus was also
positively related to performance (Fig. 5A and Table 2). For
Spontaneous Speech, a small area of the right posterior
superior temporal gyrus related positively to the scores,
along with bilateral areas of the cerebellum in lobules IV
and V (Fig. 6A and Table 2). No negative relationships
between grey matter volume and performance were
found, and no relationships between grey matter volume
and Auditory–Verbal Comprehension were identified.
To clarify the relationships between aphasia outcomes,
grey matter volume, and covariates observed in the VBM
analysis, we next performed separate hierarchical regres-
sions using the WAB-R scores as dependent measures.
PCAD, lesion size, total volume of grey matter and white
matter, age, gender, level of education and handedness
were entered first, followed by grey matter volume. The
hierarchical regression analyses showed that PCAD was
the only significant predictor of WAB-R scores when
excluding grey matter volume. When grey matter volume
was added to the models, PCAD, grey matter volume, and
age were significant predictors of all language scores. For
Repetition only, gender was also a significant predictor.
Age emerged as a significant predictor of the WAB-R
scores not because of a direct effect on scores, as evidenced
by a lack of relationship with scores when grey matter
volume was excluded, but rather because grey matter vol-
umes were inversely related to age as expected due to age-
related atrophy (AQ right hemisphere cluster R = 0.537,
P= 0.002; AQ cerebellum cluster R = 0.467, P= 0.007;
Repetition cluster R = 0.452, P= 0.009; Naming/Word-
Finding cluster R = 0.488, P= 0.005; Spontaneous
Speech right hemisphere cluster R = 0.578, P= 0.001;
Spontaneous Speech cerebellum cluster R = 0.426,
P= 0.015). For WAB-R AQ and Spontaneous Speech, the
grey matter volumes for both right hemisphere and cerebel-
lar clusters were added into the model, and in both cases,
only the right hemisphere grey matter volume was a signifi-
cant independent predictor of aphasia outcome [WAB-R
AQ right hemisphere cluster t(22) = 4.70, P= 0.0001, cere-
bellum cluster t(22) = 0.917, P= 0.37; Spontaneous Speech
right hemisphere cluster t(22) = 4.44, P= 0.0002, cerebel-
lum cluster t(22) = 1.47, P= 0.16]. Residual plots show
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relationships between grey matter volume in each of the
clusters identified above and the WAB-R scores (Figs 3B–
6B, 3C and 6C). Full regression results are provided in
Supplementary Table 2. No significant correlation was
found between the mean grey matter volumes of identified
clusters and time from stroke across patients (all P40.10).
This is expected, as all patients were at least six months
post-stroke, and there were no relationships between time
from stroke and aphasia scores in the group, controlling for
covariates as above (all P40.10).
Relationship between grey matter
volume and cognitive functions
The VBM analyses demonstrated that partially overlap-
ping regions of right temporoparietal cortex relate to
WAB-R AQ and the language production subscores. To
further specify the possible cognitive/language functions
underlying these results, we tested correlations between
the grey matter volumes in these clusters and measures
of executive function (Cognitive Linguistic Quick Test ex-
ecutive composite), verbal working memory (digit span
forward and backward), spatial working memory (Corsi
blocks forward and backward), speech praxis (Apraxia
Battery for Adults-2, subtest 2A), and output phonology
(pseudoword repetition), partialling out confounding fac-
tors (age, gender, level of education, lesion size, total
volume of grey and white matter). The partial correlation
analyses showed that the mean grey matter volumes of
the right hemisphere clusters were associated with digit
span forward (AQ right hemisphere cluster: R = 0.514,
P= 0.007; Repetition cluster: R = 0.396, P=0.045;
Figure 2 SVR-LSM beta-maps showing significant voxels associated with WAB-R score.(A–E) WAB-R AQ, Auditory-Verbal
Comprehension scores, Repetition scores, Naming/Word-Finding scores and Spontaneous Speech scores. Numbers denote MNI coordinates.
Colour bars indicate beta scores. All voxels shown in the results survived threshold at P50.001. L = left.
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Figure 3 Grey matter structure related to WAB-R AQ. (A) Grey matter volume positively correlated with AQ in the right temporoparietal
cortex (cluster 1: size = 930, peak voxel including the right supramarginal gyrus: x=60,y=37, z=35,t= 4.21 and right superior temporal gyrus:
x=60,y=45, z=22,t= 5.18, AlphaSim corrected) and left cerebellum lobules IV–VI (cluster 2: size = 342, peak voxel: x=22, y=51, z=27,
t= 3.42, AlphaSim corrected), controlling for covariates. Circles and numbers indicate clusters. R = right. (Band C) Scatter plot showing partial
regression using AQ as the dependent measure and grey matter extractions of cluster 1 or cluster 2 as the independent, controlling for covariates
(cluster 1: P=8.5810
7
; cluster 2: P=0.002). (D) Difference of marginal mean grey matter extractions between patients and controls with age,
gender, level of education and handedness as covariates of no interest.
*
P50.05, compared to controls. GM = grey matter.
Table 2 Brain grey matter related to language performance
Tests Cluster size t-values Peak MNI coordinates in mm Brain region Brodmann area
xyz
Multiple regression, Aphasia quotient as predictorgrey matter analysis (controlling for nuisance variables
a
)
930 5.18 60 45 22 Right superior temporal gyrus BA40, BA22
4.21 60 37 35 Right supramarginal gyrus BA40, BA42
342 3.42 22 51 27 Left cerebellum lobules IV, V, VI
Multiple regression, Repetition as predictor–grey matter analysis (controlling for nuisance variables
a
)
1611 5.04 60 45 23 Right superior temporal gyrus BA40, BA42, BA22
4.40 60 49 3 Right middle temporal gyrus BA21
3.48 66 25 18 Right supramarginal gyrus BA40, BA42
Multiple regression, Naming/Word-Finding as predictor–grey matter analysis (controlling for nuisance variables
a
)
449 5.40 58 45 22 Right superior temporal gyrus BA40, BA42, BA22
3.79 60 36 33 Right supramarginal gyrus BA40, BA42, BA21
Multiple regression, Spontaneous Speech as predictor–grey matter analysis (controlling for nuisance variables
a
)
331 4.64 66 43 15 Right superior temporal gyrus BA22
676 3.85 20 48 26 Left Cerebellum lobules IV, V
3.03 10 52 20 Right Cerebellum lobules IV, V
Clusters showing significant correlations with language performance. We present the size of the clusters (thresholded at P50.05, k= 323 voxels, AlphaSim corrected), MNI
coordinates of peak voxel within each cluster, brain regions and the corresponding Brodmann area (BA). The x,y,zcoordinates are according to the MNI atlas. Anatomical location
is the peak within a cluster defined as the voxel with the highest t-score.
a
Variables of no interest include age, gender, level of education, handedness, total volume of grey matter and
white matter, lesion size, proportion of critical area damaged (PCAD).
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Naming cluster R = 0.378, P= 0.057; Spontaneous Speech
right hemisphere cluster: R = 0.528, P=0.006). The
Spontaneous Speech right hemisphere clusters were also
associated with pseudoword repetition (R = 0.397,
P= 0.045). Full partial correlation results are provided
in Supplementary Table 3.
Comparison with right hemisphere
grey matter volume in control
subjects
Next to examine whether these relationships reflected neu-
roplasticity after stroke, we compared mean grey matter
volumes from aphasia patients with healthy controls in
the clusters identified in the VBM analyses. Age, gender,
level of education, and handedness were included as con-
founding variables. The marginal mean grey matter vol-
umes in patients were significantly higher than control
subjects in the right hemisphere clusters related to AQ
[F(1,56) = 5.58, P= 0.022] (Fig. 3D), Repetition
[F(1,56) = 5.01, P= 0.029] (Fig. 4C), and Naming/Word-
Finding [F(1,56) = 6.77, P= 0.012] (Fig. 5C). In contrast,
marginal mean grey matter volumes within the cerebellar
cluster related to AQ were significantly lower in patients
than control subjects [F(1,56) = 13.77, P= 0.0005]
(Fig. 3D). For Spontaneous Speech-related clusters, the
marginal mean grey matter volumes in the cerebellar cluster
was significantly lower in patients than controls
[F(1,56) = 8.26, P= 0.006], but no significant difference
was found in the right hemisphere cluster [F(1,56) = 1.74,
P= 0.193] (Fig. 6D). To ensure that local grey matter vol-
umes were not influenced by stroke-related differences in
global right hemisphere tissue volume, we repeated the
above analyses adding total right hemisphere grey and
white matter volume as a confounding variable. The results
for all right hemisphere clusters remained significant: AQ
[F(1,55) = 4.86, P= 0.032], Repetition [F(1,55) = 4.31,
P= .04] and Naming/Word-Finding [F(1,55) = 6.03,
P= 0.017].
To further ensure that these between group effects did
not reflect a general consequence of stroke regardless of
aphasia, we identified 10 patients from our other research
protocols with chronic left hemisphere stroke but no his-
tory of aphasia (Supplementary material). Controlling for
age, gender, handedness and total volume of grey matter
and white matter, the mean grey matter volumes in patients
with aphasia were significantly greater than in the 10 non-
aphasic patients in the right hemisphere clusters related to
AQ [F(1,36) = 4.84, P= 0.034], Repetition [F(1,36) = 5.73,
P= 0.022], and Naming/Word-Finding [F(1,36) = 4.95,
Figure 4 Grey matter structure related to WAB-R Repetition. (A) Grey matter volume positively correlated with Repetition score in the
right temporoparietal cortex (cluster size = 1611, peak voxel including the right superior temporal gyrus: x=60,y=45, z= 23, t= 5.04; right middle
temporal gyrus: x=60, y=49, z=3,t= 4.40; right supramarginal gyrus: x=66,y=25, z=18, t= 3.48, AlphaSim corrected), controlling for
covariates. R = right. (B) Scatter plot showing partial regression using the Repetition score as the dependent measure and grey matter extraction as the
independent controlling for covariates (P=1.2010
6
). (C) Difference of marginal mean grey matter extractions between patients and controls with
age, gender, level of education and handedness as covariates of no interest.
*
P50.05, compared to controls. GM = grey matter.
234 |BRAIN 2016: 139; 227–241 S. Xing et al.
by guest on November 10, 2016http://brain.oxfordjournals.org/Downloaded from
P= 0.032]. For Spontaneous Speech, the difference was
marginally significant [F(1,36) = 3.94, P= 0.055]. The mar-
ginal mean grey matter volumes within the cerebellar clus-
ters were significantly lower in aphasic patients than the
non-aphasic control patients [AQ cluster: F(1,36) = 6.41,
P= 0.016; Spontaneous Speech cluster: F(1,36) = 4.47,
P= 0.042].
Discussion
In the first study to examine relationships between right
hemisphere grey matter structure and language outcomes
in chronic left hemisphere stroke, we found that grey
matter volumes in the right temporoparietal cortex were
independently and positively associated with language pro-
duction outcomes after controlling for key individual dif-
ferences. In addition, grey matter volumes in the right
temporoparietal cortex were greater in left hemisphere
stroke survivors with aphasia than either healthy controls
or left hemisphere stroke survivors with no history of apha-
sia. These findings suggest right hemisphere compensation
for language deficits after stroke that is at least partly
related to beneficial right hemisphere structural plasticity
in chronic post-stroke aphasia.
Contrast with recent ideas about the
right hemisphere role in aphasia
Our results conflict with some common notions about the
role of the right hemisphere in aphasia recovery. Some have
suggested that right hemisphere recruitment depends on the
left hemisphere lesion size (Heiss et al., 2003; Heiss and
Thiel, 2006), specifically that in chronic aphasia individuals
with large lesions must rely on the right hemisphere more
than those with small lesions, and that these individuals
recover poorly because the right hemisphere is ineffective
in compensating for language deficits (Anglade et al.,
2014). However, we covaried for stroke size and found
compensatory relationships in the right hemisphere across
the group, indicating that right hemisphere compensation
was independent of lesion size. Further, grey matter vol-
umes of right temporoparietal cortex contributed significant
independent variance to speech production outcomes,
demonstrating that right hemisphere recruitment, at least
in this context, did contribute positively to aphasia
recovery.
One key factor in coming to this conclusion is that we
did not examine direct relationships between outcomes and
right hemisphere recruitment, which ignores the primary
driver of aphasia severity, the stroke itself. Instead, we
Figure 5 Grey matter structure related to WAB-R Naming/Word-Finding. (A) Grey matter volume positively correlated with
Naming/Word-Finding score in the right temporoparietal cortex (cluster size = 449, peak voxel including right superior temporal gyrus: x= 58,
y=45, z= 22, t= 5.40 and right supramarginal gyrus: x= 60, y=36, z= 33, t= 3.79, AlphaSim corrected), controlling for covariates. R = right.
(B) Scatter plot showing partial regression using the Naming/Word-Finding score as the dependent measure and grey matter extraction as the
independent, controlling for covariates (P= 1.24 10
6
). (C) Difference of marginal mean grey matter extractions between patients and controls
with age, gender, level of education and handedness as covariates of no interest.
*
P50.05, compared to controls. GM = grey matter.
Right hemisphere and aphasia outcome BRAIN 2016: 139; 227–241 |235
by guest on November 10, 2016http://brain.oxfordjournals.org/Downloaded from
first estimated the likely language outcomes based on stroke
size, PCAD and demographic factors and then asked
whether right hemisphere recruitment relates to deviations
from those predicted outcomes. By addressing the problem
in this way, we have demonstrated that right hemisphere
language homologues, at least in posterior language areas,
do contribute positively to aphasia outcomes in the chronic
phase of recovery.
This finding also conflicts with prior suggestions that
right hemisphere compensation occurs early in recovery,
but not in the chronic phase. One prominent longitudinal
study of post-stroke aphasia reported increased right fron-
tal activity correlated with language recovery in the sub-
acute period, followed later by decreased activity and a
corresponding shift back to the normal left dominance of
language in the chronic period (Saur et al., 2006). Other
studies have suggested that right hemisphere activation in
the chronic stage could be associated with poor language
recovery following stroke (Szaflarski et al., 2013). Here, we
found structural evidence of right hemisphere compensation
in the chronic period.
Differences with some previous results may relate to the
difference in the metrics of right hemisphere compensation
used. Whereas prior studies have primarily used task-
related functional brain activity to examine language net-
works in aphasia (Belin et al., 1996; Heiss et al., 2003;
Winhuisen et al., 2005; Richter et al., 2008; Allendorfer
et al., 2012), we instead examined the relationship between
grey matter structure and language. Despite its many
merits, task-related functional brain activity is related to
task difficulty (Just et al., 1996), which depends on aphasia
severity (Fridriksson and Morrow, 2005). Further, func-
tional imaging studies are only sensitive to the specific
areas of the brain activated by the particular task used.
Thus, examining brain structure rather than functional
activity may reveal different effects. One recent study exam-
ined white matter integrity in chronic post-stroke aphasia
using diffusion tensor imaging, and similarly found rela-
tionships between right hemisphere networks and language
outcomes (Forkel et al., 2014). Here, we have provided
parallel evidence for relationships between right hemisphere
grey matter structure and language outcomes. The demon-
stration of larger grey matter volumes in stroke survivors
with aphasia than both controls and stroke survivors with
no history of aphasia extends these findings to suggest that
right hemisphere compensation in aphasia occurs, at least
Figure 6 Grey matter structure related to WAB-R Spontaneous Speech. (A) Grey matter volume positively correlated with
Spontaneous Speech score in the right superior temporal gyrus (cluster size = 331, peak voxel: x= 66, y=43, z= 15, t= 4.64, AlphaSim
corrected) and left/right cerebellum lobules (cluster size = 676, peak voxel including left cerebellum: x=20, y=48, z=26, t= 3.85 and right
cerebellum: x= 10, y=52, z=20, t= 3.03, AlphaSim corrected), controlling for covariates. Circles and numbers indicate clusters. R = right.
(Band C) Scatter plot showing partial regression using Spontaneous Speech as the dependent measure and grey matter extractions of cluster 1 or
cluster 2 as the independent, controlling for covariates (cluster 1: P= 1.04 10
6
; cluster 2: P= 0.001). (D) Difference of marginal mean grey
matter extractions between patients and controls with age, gender, level of education and handedness as covariates of no interest.
*
P50.05,
compared to controls. GM = grey matter.
236 |BRAIN 2016: 139; 227–241 S. Xing et al.
by guest on November 10, 2016http://brain.oxfordjournals.org/Downloaded from
in part, through structural plasticity. Because we used a
cross-sectional design rather than a longitudinal study
with one specific intervention, our results suggest that com-
pensation by right hemisphere grey matter structures may
serve as a general mechanism of aphasia recovery.
Mechanisms of right hemisphere
compensation in aphasia
The microstructural basis of the right hemisphere grey
matter hypertrophy in these results remains unclear.
Potential mechanisms underlying grey matter plasticity in-
clude axonal sprouting, dendritic branching and synapto-
genesis, neurogenesis as well as angiogenesis (Zhao et al.,
2006). Indeed, all of these changes have been observed pre-
viously in animal models of stroke recovery (Kerr et al.,
2011) and thus may be involved here.
Our findings of compensatory hypertrophy are also in
line with structural plasticity studies showing focal changes
in grey matter during learning in healthy adults (Maguire
et al., 2000; Golestani et al., 2002; Gaser and Schlaug,
2003; Draganski et al., 2004, 2006; Taubert et al., 2010,
2012). Additionally, evidence from studies on second lan-
guage learning in healthy adults shows that second lan-
guage competence positively correlates with activations or
grey matter volume in right hemisphere networks including
the superior temporal gyrus and supramarginal gyrus
(Jeong et al., 2010; Raboyeau et al., 2010; Van Ettinger-
Veenstra et al., 2012; Hosoda et al., 2013). Studies in
stroke have also shown longitudinal changes in grey
matter volume (Dang et al., 2013; Fan et al., 2013), some-
times associated with therapy for motor deficits (Gauthier
et al., 2008) or even listening to music (Sarkamo et al.,
2014). In aphasia, constraint-induced language therapy
and melodic intonation therapy have been shown to en-
hance integrity of the right arcuate fasciculus (Schlaug
et al., 2009; Breier et al., 2010). Here we have demon-
strated that similar changes may occur in right hemisphere
grey matter morphology. Further, the changes observed
here reflect a general mechanism of language recovery in
chronic aphasia, and are not restricted to direct, potentially
transient, treatment effects.
We observed a relationship between right temporoparie-
tal grey matter volumes and speech production measures
across patients with various lesion sizes and locations.
One might expect that post-stroke plasticity would vary
based on these individual differences, as well as differences
in speech therapy and other behavioural experiences.
Indeed, there may be considerable individual differences
in patterns of recovery (Torres et al., 2013). For this
reason, we performed our whole brain analyses to identify
areas in which grey matter volumes related to aphasia out-
comes. Thus, the focality of the results is related to the
brain–behaviour relationship, not the spatial distribution
of hypertrophy across patients. Pertinent to these results,
we have previously demonstrated that right hemisphere
language activation is consistently localized across func-
tional imaging studies of aphasia using varied populations
and methods. Moreover, right hemisphere functional activ-
ity is not limited to simple one-for-one compensation by
individual nodes homotopic to the area of damage
(Turkeltaub et al., 2011). As such, one would expect
right hemisphere brain–behaviour relationships to be con-
sistently localized across individuals despite individual dif-
ferences, as we found here.
The driver for the hypertrophy in this area is likely
recruitment of alternate right hemisphere processors cap-
able of compensating for a specific language or cognitive
process involved in speech production across various spe-
cific tasks. This compensation may be a consequence of
experience, whether formal speech therapy, self-therapy
with apps or at-home exercises, or simply the experience
of struggling to communicate with aphasia. These effects
are not likely driven by a specific speech therapy experience
because the nature and quantity of speech therapy varies
between individuals substantially. Rather, various experi-
ences likely drive recruitment and hypertrophy of right
hemisphere processors capable of supporting key cogni-
tive/language functions involved in speech production
after damage in the left hemisphere language network.
Cognitive and language compensa-
tion by the right hemisphere
We found that the grey matter volumes in right temporo-
parietal areas are related to speech production, but not
comprehension. Furthermore, we identified a relationship
between the grey matter volumes of identified right hemi-
sphere regions and forward digit span, a measure of verbal
working memory capacity, as well as pseudoword repeti-
tion, which relies on both verbal working memory and
other phonological output processes. Importantly, we
allowed patients to point to a number line when respond-
ing on the digit span to ensure that scores did not simply
reflect motor speech deficits. Moreover, no relationships
were observed between grey matter volumes and other cog-
nitive faculties, including backwards digit span, which relies
on executive control of working memory. These results sug-
gest that the right hemisphere areas identified here contrib-
ute to aphasia outcomes not through broad domain general
effects on cognition, but through effects on particular as-
pects of language production, including verbal working
memory capacity and phonological output processing.
These right temporoparietal areas thus likely compensate
for homologous left hemisphere areas, as the left tempor-
oparietal cortex is commonly implicated in verbal working
memory capacity and phonological output processes.
Lesion–symptom mapping studies have implicated the left
posterior superior temporal gyrus and the supramarginal
gyrus in verbal working memory capacity (Leff et al.,
2009), phonological retrieval (Pillay et al., 2014), and
phonemic paraphasias (Schwartz et al., 2012), possibly
Right hemisphere and aphasia outcome BRAIN 2016: 139; 227–241 |237
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through a role in phonological short term storage (Baldo
and Dronkers, 2006). Similarly, neuroimaging studies have
implicated the left posterior superior temporal gyrus in
phonological access (Graves et al., 2007) and verbal work-
ing memory capacity (Richardson et al., 2011). The left
supramarginal gyrus is commonly activated during phono-
logical decision-making in healthy subjects (Devlin et al.,
2003; Buchsbaum and D’Esposito, 2009). Recent transcra-
nial magnetic stimulation studies have found that disrup-
tion of the right supramarginal gyrus interferes with
performance of phonological but not semantic decision
tasks (Hartwigsen et al., 2010), and impairs single word
production in healthy adults (Sollmann et al., 2014).
These findings suggest that right temporoparietal areas
may be good candidates to compensate for damage in the
left hemisphere language network after brain injury. These
areas may undergo hypertrophy after stroke due to
increased reliance on either learned compensatory strategies
or due to the extra verbal working memory load associated
with communicating with aphasia. Additional studies, ide-
ally using inhibitory transcranial magnetic stimulation in
people with aphasia, will help to confirm that the right
temporoparietal cortex compensates for speech production
deficits by contributing to verbal working memory capacity
and phonological output processes.
Alternate interpretations
It should be noted that our evidence for plasticity in the
right hemisphere is based on cross-sectional intergroup
comparisons. Controlling confounding factors as much as
possible, we still found robust effects suggesting structural
hypertrophy. Even stronger evidence of structural hypertro-
phy in these areas will come from future longitudinal stu-
dies. If not related to plasticity, the relationship between
right temporoparietal cortex and language outcomes likely
reflects premorbid interindividual differences in these areas
that protect from language deficits after left hemisphere
stroke. Indeed, the degree of left lateralization of language
predicts the severity of language deficits induced by transi-
ent lesions using transcranial magnetic stimulation (Knecht
et al., 2002). Here, relative bilaterality of language might
be reflected in greater grey matter volumes in the right
hemisphere, which makes an individual’s language system
more resilient in the face of left hemisphere stroke.
Alternatively, greater grey matter volumes in these areas
may reflect relatively greater premorbid ability in paralin-
guistic or attentional processes involving the right tempor-
oparietal cortex (Geranmayeh et al., 2014). Perhaps
individuals with greater premorbid attention obtain more
benefits from speech–language therapy, which could facili-
tate a better recovery and concomitant expansion of verbal
working memory capacity through other neuroplastic
mechanisms not measured here. Although additional longi-
tudinal data will strengthen the evidence for post-stroke
plasticity presented here, the current results still demon-
strate for the first time a positive association between
focal right hemisphere grey matter structure and language
outcomes after left hemisphere stroke.
Relationships with grey matter in
other undamaged areas of the brain
The VBM analyses also demonstrated a relationship be-
tween grey matter volumes in the cerebellum and language
outcomes, specifically Spontaneous Speech and as a conse-
quence, overall aphasia severity. The location of the cere-
bellar clusters is close to areas previously implicated in
speech output processes in neuroimaging studies (Callan
et al., 2007). If the atrophied areas of the cerebellum
played a role in language prior to the stroke, this could
cause additional deficits beyond those caused by the
direct damage of the stroke. Additional studies using diffu-
sion tensor imaging tractography would help to confirm the
cause of atrophy in these cerebellar areas.
Notably, we observed no relationships between grey
matter volumes and language outcomes in the left hemi-
sphere. This could be attributed to variability in stroke
distributions, which could result in different patterns of
peri-lesional compensation between stroke survivors.
However, previous studies of aphasia have shown group-
level functional activity in the left hemisphere (Fridriksson,
2010; Turkeltaub et al., 2011), suggesting that specific al-
ternate brain areas in the left hemisphere are engaged after
stroke, and that ‘peri-lesional’ left hemisphere recruitment
needn’t involve the tissue immediately adjacent to the
lesion. Perhaps distortions in spatial normalization near
the lesion or atrophy in some cortical layers due to severed
axonal connections with the lesion (Rowan et al., 2007)
interfered with the ability to identify compensatory alter-
ations of other neuronal circuits in these areas.
Conclusion
The current findings provide the first evidence that grey
matter structure in right hemisphere language area homo-
logues is associated with language recovery in chronic post-
stroke aphasia, and suggests that post-stroke hypertrophy
in these areas accounts, at least in part, for these effects.
This implies that structural plasticity in the right temporo-
parietal cortex may serve as a general compensatory mech-
anism for speech production regardless of sources of
interindividual variability in post-stroke aphasia. These
findings may provide novel targets for enhancement using
non-invasive brain stimulation techniques in chronic post-
stroke aphasia.
Acknowledgements
We thank Katherine Spiegel, Mackenzie Fama, Rachael
Harrington, Alexa Desko, Lauren Taylor, Laura Hussey,
Jessica Friedman, and Molly Stamp for contributing to
238 |BRAIN 2016: 139; 227–241 S. Xing et al.
by guest on November 10, 2016http://brain.oxfordjournals.org/Downloaded from
data collection, and our participants for their involvement
in the study.
Funding
This study was supported by the National Center for
Advancing Translational Sciences via the Georgetown-
Howard Universities Center for Clinical and Translational
Science (KL2TR000102), Doris Duke Charitable
Foundation (2012062), Vernon Family Trust, National
Basic Research Program of China (2011CB707804);
National Natural Science Foundation of China
(81000500, 81371277), Joint Funds of the Natural
Science Foundation of China (U1032005), Project
of Science and Technology New Star of Pearl River
(2012J2200089) , Chinese Government Scholarship
Program, and Alzheimer’s Drug Discovery Foundation
(20130805).
Supplementary material
Supplementary material is available at Brain online.
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