BRAINA JOURNAL OF NEUROLOGY
Anterior temporal involvement in semantic wordretrieval: voxel-based lesion-symptom mappingevidence from aphasiaMyrna F. Schwartz,1 Daniel Y. Kimberg,2 Grant M. Walker,1 Olufunsho Faseyitan,2
Adelyn Brecher,1 Gary S. Dell3 and H. Branch Coslett1,2
1 Moss Rehabilitation Research Institute, Philadelphia, PA, USA
2 Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
3 Beckman Institute, University of Illinois, Urbana-Champaign, IL, USA
Correspondence to: Myrna F. Schwartz, PhD,
Moss Rehabilitation Research Institute,
MossRehab 4th fl. Sley,
1200 West Tabor Road,
Philadelphia, PA 19141,
USA
E-mail: [email protected]
Analysis of error types provides useful information about the stages and processes involved in normal and aphasic word
production. In picture naming, semantic errors (horse for goat) generally result from something having gone awry in lexical
access such that the right concept was mapped to the wrong word. This study used the new lesion analysis technique known as
voxel-based lesion-symptom mapping to investigate the locus of lesions that give rise to semantic naming errors. Semantic
errors were obtained from 64 individuals with post-stroke aphasia, who also underwent high-resolution structural brain scans.
Whole brain voxel-based lesion-symptom mapping was carried out to determine where lesion status predicted semantic error
rate. The strongest associations were found in the left anterior to mid middle temporal gyrus. This area also showed strong and
significant effects in further analyses that statistically controlled for deficits in pre-lexical, conceptualization processes that
might have contributed to semantic error production. This study is the first to demonstrate a specific and necessary role for
the left anterior temporal lobe in mapping concepts to words in production. We hypothesize that this role consists in the
conveyance of fine-grained semantic distinctions to the lexical system. Our results line up with evidence from semantic
dementia, the convergence zone framework and meta-analyses of neuroimaging studies on word production. At the same
time, they cast doubt on the classical linkage of semantic error production to lesions in and around Wernicke’s area.
Keywords: aphasia; voxel-based lesion-symptom mapping; naming; semantic; errors
Abbreviations: NVcomp = non-verbal comprehension composite score; MNI = Montreal Neurological Institute; SemErr = theproportion of semantic errors; Vcomp = verbal comprehension composite score
IntroductionA common symptom of spoken language impairment in aphasia is
the loss of precision in the mapping from concepts to words in
production, manifesting in semantic errors (e.g. misnaming ‘sofa’
as ‘chair’ or ‘elephant’ as ‘zebra’). These errors have garnered
considerable attention from researchers in many disciplines,
in part because healthy speakers also make them, albeit
less frequently. Our study employed the new lesion analysis
technique known as voxel-based lesion-symptom mapping
doi:10.1093/brain/awp284 Brain 2009: 132; 3411–3427 | 3411
Received June 1, 2009. Revised September 1, 2009. Accepted September 24, 2009
� The Author (2009). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
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(Bates et al., 2003) to investigate the locus of lesions that give rise
to semantic errors.
Two common assumptions about aphasia motivated this
work: (i) analysis of error types is a critical component of the
characterization of language impairments (Blumstein, 1973;
Butterworth, 1979; Buckingham, 1980; Saffran, 1982; Howard
and Orchard-Lisle, 1984; Caramazza, 1986; Caramazza and
Hillis, 1990; Plaut and Shallice, 1993; Schwartz et al., 1994;
Nickels, 1995; Rapp and Goldrick, 2000; Wilshire, 2002) and
(ii) heightened error tendencies in aphasia result from parametric
change in the premorbid cognitive and neural architecture of
language systems, rather than from a fundamental restructuring
of those networks (Caramazza, 1986; Plaut et al., 1996; Dell
et al., 1997; Laine et al., 1998; Foygel and Dell, 2000; Rapp
and Goldrick, 2000; Schwartz et al., 2006). The ultimate goal,
then, was to understand the neural architecture of semantic
word processing better by investigating where in the brain lesions
give rise to semantic production errors.
Interest in lesion localization has fluctuated in recent years.
In the flush of enthusiasm that accompanied the first generation
of functional neuroimaging research, the traditional methods of
lesion mapping seemed too fraught with bias and imprecision to
compete with the new method. The cognitive neuroscience of the
21st century was expected to be much more about functional
neuroimaging than neuropsychology. Recently, however, more
balanced appraisals have appeared that weigh the strengths and
weaknesses of lesion methods against those of functional MRI and
conclude that each has something unique to offer (Rorden and
Karnath, 2004; Chatterjee, 2005; Fellows et al., 2005; Kimberg
et al., 2007). These arguments, along with advances in spatial
registration of lesioned brains (Brett et al., 2001; Stamatakis and
Tyler, 2005) and statistical testing and thresholding of lesion
effects (Kimberg et al., 2007; Rorden et al., 2007; Rudrauf
et al., 2008; Glascher et al., 2009) have revived interest in
lesion-symptom mapping and its potential to reveal which brain
areas must retain their integrity in order for performance of a
given task or condition to be performed at normal levels of
proficiency.
Voxel-based lesion-symptom mappingTraditional approaches to lesion mapping typically reduce lesion
data by binary grouping of patients (with and without lesions in
one or more areas of interest) and/or some form of aggregation
(calculating percent damage in areas of interest). Often, the
behavioural data are also reduced to represent only the presence
or absence of some deficit of interest. While powerful, these
methods are often limited by the use of arbitrary criteria to dichot-
omize data, and the need to pre-identify regions of interest.
In the case of overlap mapping, the analysis generally results in
a non-statistical comparison.
In voxel-based lesion-symptom mapping, the association
between a particular behavioural score (e.g. proportion of
verb-naming errors) and lesion status (� lesion in that voxel) is
calculated across a group of patients at each voxel, and the effect
at each voxel is evaluated for significance using a threshold
corrected for multiple comparisons (e.g. Tsuchida and Fellows,
2009). This approach does not require reducing either the
amount of lesion data or behavioural data and, in a large and
diverse patient sample, can potentially detect effects across the
whole brain. A variety of methods are available to narrow down
the interpretation and rule out nuisance variables. For example, in
a voxel-based lesion-symptom mapping study aimed at localizing
verb-specific naming deficits, effect-size maps were generated for
verb- and for noun-naming, and the latter was subtracted from
the former (Piras and Marangolo, 2007). An alternative approach
would have been to use a regression framework whereby at each
voxel the noun-naming score is treated as a nuisance covariate in
a model that predicts verb scores from lesion status. In the present
study, we used the regression framework to isolate semantic errors
that originate during production from those that arise during
conceptualization.
Semantic production errorsPsycholinguistic models of aphasia recognize that semantic errors
are multi-determined. For example, one might say ‘truck’ in
response to the picture of a bus because of confusion about
what the picture depicts (visual system), or what it means (seman-
tic system). In the latter case, the error could reflect deficiencies in
how bus and truck are represented as concepts (semantic repre-
sentational deficit) or deficiencies in how such conceptual repre-
sentations are accessed by executive control processes (semantic
access deficit). All the aforementioned cases bear on the concep-
tualization of the target of naming. Semantic errors arising during
conceptualization are of secondary interest in this study. The
primary focus is on semantic errors that arise during production,
specifically in the mapping from semantics to lexical items.
Semantic production errors arise when a correct semantic repre-
sentation (e.g. of the bus concept) is mapped to the wrong word
(e.g. truck) by virtue of the overlap in their semantic representa-
tions. Models of normal error performance have successfully
simulated semantic error probability on the assumption that
errors arise in the mapping to lexical representations (Dell et al.,
1997). Furthermore, the fact that such errors also appear to be
constrained to match on grammatical category provides
further evidence that they occur during lexical access
(e.g. Garrett, 1975).
To clarify the conceptualization versus production distinction as
used here, Fig. 1 shows two influential accounts of lexical access in
production. In both, the first lexical representation that is accessed
during word production is an abstract, pre-phonological word
form, or ‘lemma’ (Kempen and Huijbers, 1983). Semantic errors
in production happen when a wrong item is selected at this stage
without anything having gone wrong at the prior stage. For exam-
ple, in the interactive two-step model, with cat as the target
and the semantic features for cat having been activated
normally, the selection of the word node for dog would constitute
a semantic production error. In Levelt et al.’s model (1999), this
corresponds to the lexical concept for cat being erroneously
mapped to the lemma for dog. In contrast, semantic errors in
conceptualization happen when, for whatever reason, the
wrong lexical concept/semantic features are selected, e.g. with
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cat as target, the lexical concept for dog is selected and mapped
(correctly) to the lemma for dog.
Neurology of semantic word processingLesion studies of aphasia have long implicated the left posterior
temporal lobe in the genesis of semantic errors. Wernicke’s
aphasia and transcortical sensory aphasia are characterized by
fluent speech, poor naming with a predominance of semantic
errors and poor comprehension. They are distinguished by
repetition, which is spared in transcortical sensory aphasia.
Both are considered as left posterior syndromes. Patients with
Wernicke’s aphasia generally have lesions in posterior superior
and middle temporal gyri (Wernicke, 1874; Luria, 1976;
Damasio, 1981). Transcortical sensory aphasia has also been
localized to posterior superior and middle temporal gyri
(Boatman et al., 2000), but more commonly, lesions are found
in surrounding sites in the temporal–parietal–occipital junction
(portions of Brodmann area 39 and 19) or the inferior-middle
temporal region (Brodmann area 37) (Heilman et al., 1981;
Kertesz et al., 1982; Alexander et al., 1989).
In an elegant series of neurolinguistic studies with acute
ischaemic stroke patients, Hillis and colleagues (2001a; DeLeon
et al., 2007) extended and refined the evidence for posterior tem-
poral involvement in semantic word processing and semantic error
production, in particular. Using region of interest analysis of MRI
perfusion- and diffusion-weighted imaging, they showed that
within 24 h of the neurological event, a Wernicke-like pattern of
poor spoken word comprehension and poor oral naming corre-
lated with hypoperfusion in Brodmann area 22, whereas poor
naming with spared word comprehension correlated with
hypoperfusion in Brodmann area 37. Pharmacologic reperfusion
produced improvements in accordance with the observed correla-
tions, i.e. improved comprehension and naming correlated with
reperfusion of Brodmann area 22; improved naming alone corre-
lated with reperfusion of Brodmann area 37 (Hillis et al., 2001b,
2006). The most recent paper in this series (Cloutman et al., 2009)
identified acute patients whose errors in oral naming were
predominantly semantic (let us designate this group S+) and fur-
ther classified them as to whether their semantic comprehension
was impaired (S+/+) or spared (S+/�). Membership in the S+/+
group was predicted by tissue dysfunction in Brodmann area 22;
membership in S+/� was predicted by tissue dysfunction in
Brodmann area 37. These acute stroke studies offer persuasive
evidence that damage to posterior temporal regions disrupts the
mapping between concepts and words in production, with and
without accompanying deficits in verbal comprehension.
However, the story is apparently more complex. Semantic
production errors occur in normal speakers, as we have said,
and in all types of aphasia, including the anterior syndromes
(Schwartz et al., 2006). When one looks beyond the aphasia
literature to neuropsychological research with other patient popu-
lations and to neuroimaging studies of normal language, it appears
that the mapping from semantics to words in production takes
place over an extensive left-lateralized network that may include
parts of the middle temporal lobe and anterior temporal lobe and
Figure 1 The sequence of processing steps in picture naming, according to the theory of Levelt et al. (1999) and Dell’s Interactive
two-step model (Dell and O’Seaghdha, 1991). The diagram of Dell’s computational model shows what happens on a naming trial in
which the target is cat. Visual and conceptual processes (not shown) result in semantic features for cat becoming activated. Due to
feature sharing, activation spreads to the word node (lemma) for cat and, to a lesser degree, those for dog and rat. In the resulting
competition for word selection, the target word will typically emerge as the winner, and its corresponding phonemes will be retrieved at
the next step. However, due to noisy activations or pathology-induced weakness in s-weights, dog or rat will sometimes win, and the
result will be a semantic error. Most semantic errors are thought to arise in this way, i.e. the right lexical concept activates the wrong
lemma, but we acknowledge that things can also go awry during conceptual preparation that result in the wrong lexical concept being
selected (picture wrongly conceptualized as a dog) and its corresponding name produced.
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inferior and dorsolateral prefrontal cortices, in addition to the pos-
terior and inferior temporal lobe (e.g. Damasio and Tranel, 1993;
Damasio et al., 1996, 2004; Indefrey and Levelt, 2000, 2004;
Maess et al., 2002; Duffau et al., 2003, 2005; Schnur et al.,
2009). Some of these cortical regions are rarely, if ever, affected
by middle cerebral artery infarctions that are the most frequent
cause of aphasia, so it is understandable that they would have
escaped detection in lesion mapping studies with this population.
Indeed, the large neuroanatomical study of category-specific
naming deficits that presented the earliest and perhaps most per-
suasive evidence for the necessary role of anterior inferotemporal
regions (Damasio et al., 1996, 2004) included patients with herpes
simplex encephalitis and temporal lobectomy in addition to infarc-
tion. On the other hand, some of these anterior cortical regions do
fall within the territory of the middle cerebral artery and may have
been missed in the past due to surmountable problems such as
biased selection criteria or small sample size.
Motivated by the important role that semantic naming errors
have assumed in psycholinguistic and neurolinguistic models of
semantic processing and lexical access (e.g. Caramazza and
Hillis, 1990; Rapp and Goldrick, 2000; Schnur et al., 2006;
Schwartz et al., 2006; Cloutman et al., 2009) and by the
unresolved questions and controversies surrounding the role of
the left anterior temporal lobe in semantic-lexical mapping systems
(Murtha et al., 1999; Wise, 2003; Hickok and Poeppel, 2004),
we conducted a lesion study of semantic naming errors using
voxel-based methods. By recruiting a large and diverse group of
patients, we managed to obtain adequate coverage of middle and
anterior temporal lobe structures, as will be shown. Our study
further sought to determine whether anterior temporal lobe
lesions would predict semantic error proportions after filtering
out performance on semantic comprehension tests. Evidence that
the anterior temporal lobe effect survives such filtering would bol-
ster the notion that anterior temporal lobe plays an essential role
in the genesis of semantic errors, in production, specifically.
Methods
SubjectsPatients who had been diagnosed acutely with aphasia secondary to
left hemisphere stroke were recruited from the Neuro-Cognitive
Rehabilitation Research Patient Registry at the Moss Rehabilitation
Research Institute (Schwartz et al., 2005) or the Centre for
Cognitive Neuroscience Patient Database at the University of
Pennsylvania. Eighty-two patients gave informed consent to take
part in a multi-session language assessment under protocols approved
by the Institutional Review Boards at Albert Einstein Medical Centre
and the University of Pennsylvania School of Medicine. All but two of
these also gave informed consent to undergo structural MRI or CT
imaging of the brain in order to determine the precise localization
of their lesion. For the remaining two, a recently performed clinical
imaging study judged to be of high quality was used instead. Imaging
studies were conducted under the protocol of the University of
Pennsylvania School of Medicine and were conducted at that facility.
Participants were paid for their participation and reimbursed for travel
and related expenses.
To be accepted into the study, participants had to meet the
following criteria: no major psychiatric or neurologic co-morbidities;
pre-morbid right handedness; English as primary language; adequate
vision and hearing without or with correction; some ability to name
pictures; and CT or MRI confirmed left hemisphere cortical lesion.
Of the 82 who consented, 18 were disqualified from participating:
9 because they failed to produce any correct naming responses and
9 because their scans revealed bilateral damage or damage restricted
to sub-cortical areas. The 64 who were included in the study (42%
females; 48% African American) had a mean age of 58 (range 26–78),
mean years of education of 14 (10–21) and mean months post-onset
of 68 (1–381). Ninety-two percent of participants were at least
6 months post-onset. All were living in the community at the time
of testing.
Language testsThe 175-item Philadelphia Naming Test (Roach et al., 1996) was used
to measure semantic error production in picture naming. The black and
white pictures represent non-unique entities from varied semantic
categories, the largest being manipulable objects (41%) and animals
(15%). Pictures have high familiarity, name agreement and image
quality. Names range in length from 1 to 4 syllables and in noun
frequency from 1 to 2110 tokens per million (Francis and Kucera,
1982).
Standard procedures were used to administer the Philadelphia
Naming Test and classify errors (http://www.ncrrn.org/assessment/
pnt; see also Dell et al., 1997; Schwartz et al., 2006). On each trial,
the first complete (i.e. non-fragment) response produced within 20 s
was scored. The current study focuses on errors classified as semantic;
these are real word responses that constitute a synonym, category
coordinate, superordinate, subordinate or strong associate of the
target (e.g. vase for bowl; rose for flower). The number of semantic
errors was divided by the number of trials (175) to generate the
dependent variable ‘SemErr’.
Two tests of non-verbal semantic comprehension were adminis-
tered. The Pyramids and Palm Trees Test (Howard and Patterson,
1992) and the Camel and Cactus Test (Bozeat et al., 2000; Lambon
Ralph et al., 2001a) both involve picture–picture matching based on
thematic relatedness (e.g. wine-grape). The 52-item Pyramids and
Palm Trees Test requires a two-choice match; the 64-item Camel
and Cactus Test requires a more demanding four-choice match.
These two tests were scored for accuracy (percent correct), standar-
dized by z-scores and averaged to create a non-verbal comprehension
composite score, which we call ‘NVcomp’. NVcomp was used to con-
trol for semantic errors in naming that arise from selection of the
wrong lexical concept/semantic features at the end of the conceptu-
alization stage (Fig. 1).
We also administered two verbal comprehension tests. The Peabody
Picture Vocabulary Test (Third edition-form A) (Dunn and Dunn,
1997) consists of 204 trials in which a spoken word must be matched
to one of four pictures that best represents its meaning. The Synonym
Judgement Test consists of 30 trials (half nouns, half verbs) on which
three printed words are arrayed and read aloud and the subject must
decide which two mean the same (Martin et al., 2005). These tests
were scored for accuracy (percent correct), standardized by z-scores
and averaged to create a verbal comprehension composite score,
which we call ‘Vcomp’. Vcomp was used to control for any contribu-
tion to semantic errors in naming that arises from an impairment in the
ability to access the meaning of a word from one of its input lexical
representations (e.g. semantic errors in production may be more likely
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if one fails to detect—through comprehension processes—
potential semantic errors, preventing them from being overtly spoken).
Various supplementary tests were also administered, including the
Western Aphasia Battery (Kertesz, 1982) and tests for verbal apraxia,
word and non-word repetition, auditory input processing, short-term
memory strength and syntactic comprehension. Additional assessments
were done to confirm right-hand dominance (Oldfield, 1971) and
adequate hearing (Ventry and Weinstein, 1983).
Imaging methodsStructural images were acquired using MRI (n = 34) or CT (n = 30).
Thirty-two patients were scanned on a 3.0 T Siemens Trio scanner.
High-resolution whole-brain T1-weighted images were acquired
(repetition time = 1620 ms, echo time = 3.87 ms, field of
view = 192� 256 mm, 1� 1�1 mm voxels) using a Siemens 8-channel
head coil. In accordance with established safety guidelines (MRI safety;
www.mrisafety.com), two patients were scanned on a 1.5 T Siemens
Sonata because of an implant that had not been approved for a 3T
environment. Whole-brain T1-weighted images were acquired (repeti-
tion time = 3000 ms, echo time = 3.54, field of view = 24 cm) with a
slice thickness of 1 mm using a standard radio-frequency head coil.
As MRI was contra-indicated for the remaining 30 patients, they
underwent whole-brain CT scans without contrast (60 axial slices,
3 mm thick) on a 64-slice Siemens SOMATOM Sensation scanner.
Voxel-based lesion-symptom mappingmethods
Lesion segmentation and warping to template
For patients with high-resolution MRI scans available electronically
(n = 34), lesions were segmented manually on a 1�1�1 mm
T1-weighted structural image. The structural scans were registered to
a common template using a symmetric diffeomorphic registration
algorithm (Avants et al., 2006; see also http://www.picsl.upenn.edu/
ANTS/). This same mapping was then applied to the lesion maps.
To optimize the automated registration, volumes were first registered
to an intermediate template constructed from images acquired on the
same scanner. A single mapping from this intermediate template to
the Montreal Neurological Institute (MNI) space ‘Colin27’ volume
(Holmes et al., 1998) was used to complete the mapping from subject
space to MNI space. The final lesion map was quantized to produce a
0/1 map, using 0.5 as the cut-off value. After being warped to MNI
space, the manually drawn depictions of the lesions were inspected by
H.B.C., an experienced neurologist who was naıve with respect to
the behavioural data.
For patients with CT scans (n = 30), H.B.C., naıve to the behavioural
data, drew lesion maps directly onto the Colin27 volume, after rotat-
ing (pitch only) the template to approximate the slice plane of the
patient’s scan. We have previously demonstrated excellent intra- and
inter-rater reliability with this method (Schnur et al., 2009).
Voxel-based lesion-symptom mapping
Voxels in which fewer than five patients were lesioned were excluded
from the voxel-based lesion-symptom mapping analyses. A simple
t-test comparing the scores between patients with and without lesions
was performed at each voxel using the VoxBo brain imaging package
(www.voxbo.org). The resulting t-map was thresholded to control the
false discovery rate (Genovese et al., 2002) at q = 0.01, where q is the
expected proportion of false positives among supra-threshold voxels.
Imaging results reported below use this as the threshold for
significance.
As stated earlier, our aim was to localize lesions that give rise to
semantic errors in the mapping from concepts to words in production.
To control for semantic errors in production that arise instead from
faulty conceptualization, we performed an analysis in which NVcomp
scores were factored out of the SemErr measure by regression. To
control for semantic errors in production that may depend on
a verbal comprehension component to the production process, we
performed a second analysis in which Vcomp scores were factored
out of the SemErr measure. The removal of NVcomp and Vcomp
was done within the statistical package R (www.r-project.org).
Results
Language testsResults for key language measures are shown in Table 1.
The Western Aphasia Battery identified 6 of the 64 patients as
‘recovered’ (AQ493.8) (Kertesz, 1979), although all 6 scored out-
side the control range on other tests in the battery. The remaining
58 were classified as follows: anomic (n = 24), Broca’s (n = 19),
conduction (n = 10), Wernicke’s (n = 4), transcortical motor
(n = 1). The under-representation of Wernicke’s aphasia (7%)
and transcortical sensory aphasia (0%) is to be expected in
chronic, unselected samples and reflects the tendency for these
subtypes to evolve into anomic or conduction aphasia as the
early symptoms—neologistic jargon and profound comprehension
deficit—recover (Kertesz and Benson, 1970; Kertesz and McCabe,
1977; Goodglass and Kaplan, 1983).
On the Philadelphia Naming Test, there was a wide range in the
proportion of items correct (0.02–0.97), with the median falling at
0.80, which is outside the normal range. The proportion of seman-
tic errors (SemErr) produced was maximal at 0.12. This is roughly
comparable with other large, unbiased aphasia samples (e.g. Dell
et al., 1997; Schwartz et al., 2006). Not surprisingly, studies that
select for semantic error production or employ criteria that bias
towards semantic impairment tend to report higher semantic error
frequencies (Ruml et al., 2005; DeLeon et al., 2007). Relative to
total errors (instead of total trials), semantic error production
ranged from 0.00 to 0.77 (SemErr/TotErr in Table 1). The purest
semantic error patterns (0.30 or more SemErr/TotErr) occurred
exclusively in the most accurate patients (0.70 or more correct).
The four comprehension tests all yielded scores below
the mean for healthy elderly controls, as shown in Table 1. The
two non-verbal tests correlated strongly with one another,
(r = 0.79, P50.001), as did the two verbal tests (r = 0.64,
P50.001). The correlation between NVcomp and SemErr was
�0.44 (P50.001) and that between Vcomp and SemErr, �0.27
(P = 0.03).
Characterization of lesion dataAfter excluding voxels with fewer than five lesions, the number of
voxels that qualified for analysis was 404 565, or 55% of the
738 535 voxels in the left hemisphere (using counts from the
electronic AAL atlas) (Tzourio-Mazoyer et al., 2002). This included
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83 096 distinct patient-lesion patterns, in which each such pattern
is defined by the subset of patients lesioned in a voxel. The
number of distinct voxels is maximal for lesion counts around
16, a quarter of the total patients. The number of voxels with
between 27 and 37 lesions (37 was the maximum) was 47 558,
representing 11 482 patterns.
In voxel-based lesion-symptom mapping, differences in power
between regions are due to differences in the frequency with
which lesions impinge the region. Maximal power is achieved in
voxels lesioned in half the patients (32 in the present dataset).
Figure 2 shows a colour map of the number of patients with
lesions in each voxel and suggests the relative (not absolute)
Figure 2 Maps depicting lesion overlaps of the 64 subjects in the left hemisphere. Maps (A—D) are at MNI x coordinates of �60,
�54, �48 and �42, respectively. Map (E) is a single axial slice at z =�27. The regions of maximal overlap (bright yellow) are in the
peri-sylvian regions, particularly anterior. Only voxels lesioned in at least five subjects were included.
Table 1 Language test data and control norms
Participants with aphasia (n = 64) Norms for healthy controls
Test/measure Mean (SD) Median Low HighMean (SD)
Aphasia quotient 76.8 (15.2) 81.5 33.3 97.6 93.8a,*
Philadelphia Naming Test:
Proportion Correct 0.69 (0.26) 0.8 0.02 0.97 0.97 (0.018)b
SemErr 0.03 (0.03) 0.03 0.00 0.12 n/a
SemErr/TotErr 0.17 (0.15) 0.14 0 0.77 n/a
Nonverbal comprehension:
Pyramids and Palm Trees Test (pictures; max. 52) 46.4 (5.0) 47.8 24 52 51.2 (1.4)c
Camel and Cactus Test (pictures; max. 64) 50.1 (7.6) 51.8 23 61 58.4 (3.4)d
Composite measure (NVcomp; mean of z-scores) �0.04 (1.04) 0.16 �4.0 1.16 n/a
Verbal comprehension:
Peabody Picture Vocabulary Test (204 items; standard score) 82.7 (14.9) 85 40 118 100 (15)e
Synonym Judgment Test (max. 30) 24.7 (4.7) 26.1 10 30 29.1 (.01)f
Composite measure (Vcomp; mean of z-scores) 0.002 (0.91) 0.18 �2.81 1.48 n/a
a Kertesz, 1982.b Unpublished; n = 20 healthy older controls.
c Bozeat et al., 2000.d Lambon Ralph et al., 2001.e Dunn and Dunn, 1997.f Martin et al., 2005.*Cut-off score; n/a = not available; SemErr/TotErr = semantic error production relative to total errors.
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power of each voxel for detecting an association, if one exists,
between lesioned status and the behavioural measures.
There are obvious and predictable limitations in our coverage.
As aphasia is typically associated with strokes in the middle
cerebral artery territory, we are unable to explore the contribution
of brain regions typically supplied by the posterior or anterior
cerebral arteries. These regions include the mesial portions of the
hemisphere as well as the occipital lobe, posterior inferior temporal
lobe and mesial temporal lobe. On the other hand, the entire
peri sylvian region had good coverage. For example, in the left
inferior frontal gyrus (Brodmann area 44/45) voxels with as
many as 35 lesions were identified. The maximum number of
lesions in the posterior superior temporal lobe and the superior
portion of Brodmann area 37 were 28 and 24, respectively.
Finally, it is important to note that voxels with substantial
numbers of lesions were identified in the temporal pole
(Brodmann area 38) and anterior middle temporal gyrus
(Brodmann area 21); in the former, the maximal lesion count
was 19, 16 in the latter.
The map in Fig. 3 depicts t-values for the difference in total
lesion volume between patients with and without damage at
each voxel. It shows that local lesion status is highly predictive
of overall lesion volume in most of the left hemisphere—the
relationship is no stronger in the anterior temporal lobe than
elsewhere in the anterior half of the left hemisphere. In effect,
lesion size was not strongly predicted by lesion location in this
sample.
Anatomic findingsIn the analysis performed to explore the anatomic basis of the
variable SemErr, we found 35 466 voxels for which a significant
correlation between lesion status and impaired performance on
the SemErr measure was identified. As indicated in Fig. 4, the
voxels with the highest t-values were located in the anterior tem-
poral lobe. The highest concentration of significant voxels (19 411
voxels) was in the anterior half of the middle temporal gyrus and
the temporal pole, in Brodmann area regions 21 and 38, respec-
tively. A second distinct cluster of significant voxels was located in
the posterior portion of the middle temporal gyrus, corresponding
to the lateral and superior portion of Brodmann area 37 at approx-
imately the termination of the middle temporal gyrus/occipital
junction. There was also a smaller cluster of significant voxels in
the left lateral prefrontal cortex; they were primarily located in the
inferior and middle frontal gyri, corresponding to Brodmann areas
45 and 46. There were also a small number of significant voxels in
the deep white matter. As indicated in Fig. 4, there were no sig-
nificant voxels in the posterior superior temporal gyrus, corre-
sponding to Wernicke’s area; indeed, the peak t-values observed
in this region were around 1.8, far below the critical t (3.27).
Filtering out Vcomp, as described above, changed the strength
of effects slightly but not the overall pattern (Fig. 5). The major
change was a reduction in the number of significant voxels in the
lateral prefrontal cortex (Brodmann area 45/46). Of the 26 771
voxels that exceeded the critical threshold after the filtering, the
Figure 3 Maps of the reliability (t-statistic) of the difference in lesion volume between patients with and without lesions in each voxel
(rendered on the MNI-space ch2 volume). Voxels exceeding an arbitrary threshold of 4 are rendered in a red (t = 4) to yellow (t47)
scale, while voxels below t = 4 are rendered on a scale from green (t just below 4) to blue (t = 0 or below). Maps (A—D) are at MNI
x coordinates of �60, �54, �48 and �42, respectively. Map (E) is a single axial slice at z =�27. Note that a false discovery rate-
corrected significance threshold for this map would be at 2.58. The colour scale for this figure was selected to accentuate regional
differences.
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majority (16 427) were concentrated in the anterior temporal lobe
and middle portion of the middle temporal gyrus, defined
arbitrarily as that portion of the temporal lobe anterior to a
y-value of �35.
Filtering out NVcomp had a more substantial impact on the
strength and pattern of results (Fig. 6). No voxels exceeded
threshold in either Brodmann area 45/46 or Brodmann area 37.
Of the 6366 voxels exceeding threshold, the majority (4361) were
in the anterior temporal lobe region described above.
We investigated the anterior temporal lobe effect further, first
by inspecting all the raw (pre-registered) scans for evidence of
lesions within the anterior temporal lobe region defined in the
unfiltered analysis. Thirty-four, more than half the sample, had
readily identified damage here (see Fig. 7 for examples).
Second, we pulled out the 20 patients with the most semantic
errors and the 18 with the fewest (to avoid ties) and constructed
an overlap map showing, in each voxel, the proportion of lesions
in the high SemErr group minus the proportion of lesions in the
low SemErr group. As Fig. 8 shows, the voxels with large differ-
ences occupy the previously identified anterior temporal lobe and
lateral prefrontal areas. Although the threshold for the overlap
map is arbitrary, these two regions show the most robust,
coherent overlap differences. Some additional voxels in primary
sensory-motor cortices are also apparent.
In the final set of analyses, we further investigated the partial
confound between anterior temporal lobe damage and lesion size.
The strong association between total lesion volume and localized
damage (in most of the covered voxels, including the anterior
temporal lobe) means that partialing out volume would result in
very low statistical power to detect independent effects at a voxel-
wise level. However, as a first approximation, we carried out that
analysis—regressing out total lesion volume from SemErr—and
mapped the voxels that exceeded the critical threshold without
correction (cf Karnath et al., 2004). Once again, the anterior tem-
poral lobe involvement was most prominent (Fig. 9), although the
frontal involvement was somewhat reduced. Moreover, we statis-
tically confirmed the independent contribution of anterior tempo-
ral lobe in a regional analysis, in which we calculated the
percentage of damage within Brodmann areas 38 (temporal
pole) and 21 (middle temporal gyrus) and computed partial
Figure 4 Maps of the reliability (t-statistic) of the difference in SemErr between patients with and without lesions in each voxel
(rendered on the MNI-space ch2 volume). Voxels exceeding the false discovery rate threshold (q = 0.01) are rendered in a red (t = 3.27)
to yellow (t45) scale, while non-significant values are rendered on a scale from green (t just below the threshold) to blue (t = 0 or
below). Maps (A—D) are at MNI x coordinates of �60, �54, �48 and �42, respectively. Map (E) is a single axial slice at z =�27. The
peak value of 5.41 was held in common by 250 voxels, 201 of which formed a contiguous region centred at MNI coordinates �54,
�18, �18—the middle portion of the middle temporal gyrus (indicated by the crosshairs in map B). Panel (F) depicts SemErr scores for
patients with and without damage to the anterior temporal lobe (ATL) region identified in this analysis. Voxels were included if they
were part of the region of contiguous supra-threshold voxels including the anterior temporal lobe. Error bars show 2 SEM above and
below the means.
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Figure 6 Maps of the reliability (t-statistic) of the difference in SemErr, after factoring out NVcomp, between patients with and
without lesions in each voxel (rendered on the MNI-space ch2 volume). Voxels exceeding the false discovery rate threshold (q = 0.01)
are rendered in a red (t = 3.82) to yellow (t45) scale, while non-significant values are rendered on a scale from green (t just below
threshold) to blue (t = 0 or below). Maps (A—D) are at MNI x coordinates of �60, �54, �48 and �42, respectively. Map (E) is a single
axial slice at z =�27. The peak statistical value of 4.97 is held by two contiguous voxels at MNI coordinates �58, �11, �17
(a far anterior voxel in the temporal pole).
Figure 5 Maps of the reliability (t-statistic) of the difference in SemErr, after factoring out VComp, between patients with and without
lesions in each voxel (rendered on the MNI-space ch2 volume). Voxels exceeding the false discovery rate threshold (q = 0.01) are
rendered in a red (t = 3.36) to yellow (t45) scale, while non-significant values are rendered on a scale from green (t just below
threshold) to blue (t = 0 or below). Maps (A—D) are at MNI x coordinates of �60, �54, �48 and �42, respectively. Map (E) is a single
axial slice at z =�27. The centroid of the peak voxels (t = 5.47) in the map were at �54, �18, �18 (exactly the same region as in the
unfiltered analysis).
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correlations with SemErr, controlling for total lesion volume.
Results were significant for both areas (Brodmann area 21:
r = 0.34, P = 0.006; Brodmann area 38: r = 0.33, P = 0.008), indicat-
ing that damage here correlated with semantic error production
above and beyond the contribution of lesion size.
DiscussionWhile picture naming is considered among the simplest of
language tasks, it is cognitively complex, as indexed by the
types of memory representations accessed, the levels of processing
involved and the number of processing parameters required to
simulate normal performance (Dell et al., 1997; Levelt et al.,
1999). Research in the neuroanatomy of naming has become
increasingly sophisticated in relating regional activation or sites
of damage to psycholinguistic models (Murtha et al., 1999;
Indefrey and Levelt, 2000, 2004; Moss et al., 2005; Price et al.,
2005; DeLeon et al., 2007; Graves et al., 2007; Postman-
Caucheteux et al., 2009). Extending this model-based approach,
our study sought evidence on voxel-wise localization of lesions
that give rise to semantic naming errors that arise during the
process of mapping concepts to words.
The first (unfiltered) analysis identified significant voxels in three
distinct areas, of which the strongest was the anterior temporal
lobe. To control for deficits in pre-lexical conceptualization pro-
cesses that might have contributed to semantic error production,
we then regressed out scores on the composite comprehension
measures Vcomp and NVcomp. Controlling for Vcomp in this
manner made little difference, while controlling for NVcomp elimi-
nated effects in the areas outside of anterior temporal lobe. Thus,
the conclusion from the main analyses is that the symptom of
interest—semantic errors generated at the word production
stage—arises from damage to anterior temporal lobe. For the
other two areas, Brodmann areas 37 and 45/46, an alternative
basis for semantic errors is indicated. We return to this issue
later in the Discussion section.
Could the anterior temporal lobe effect be spurious? A potential
concern is that anterior temporal lobe damage in our sample might
represent the peripheral extent of very large lesions, and what
manifests as an anterior temporal lobe effect might instead be
due simply to lesion size. This is unlikely for several reasons.
First, far from being limited to a few patients, anterior temporal
lobe lesions were evident in the raw scans of more than half the
participants (34 of 64). While anterior temporal lobe damage may
be rare in the stroke population at large (Wise, 2003; Noonan et
al., in press), it is apparently quite common among chronic stroke
patients with persisting aphasia. Second, the association between
lesion status and lesion size was high in the anterior temporal lobe,
but not so more than in other areas that were not associated with
SemErr (Fig. 3). Finally, and most compellingly, in Brodmann areas
38 and 21—areas where significant voxels were identified—partial
correlation analysis revealed associations between the amount of
damage and SemErr score that were statistically independent of
the contribution of total lesion volume.
Role of left anterior temporal lobe inword productionThese data provide compelling evidence that lesions within ante-
rior temporal lobe give rise to semantic errors in word production.
What is the basis for this effect? The voxels with highest t-values
were clustered in the mid-part of the middle temporal gyrus. This
location agrees remarkably well with a meta-analysis of the ima-
ging literature on word production (Indefrey and Levelt, 2000,
2004). Drawing on a model of lexical access in speech production
(Roelofs, 1992; Levelt et al., 1999), Indefrey and Levelt (2000)
identified the computational stages of word processing tapped in
activation tasks from 58 experiments, and then determined the
spatial overlap of activated regions corresponding to one or
another processing stage. In the 2004 publication, newer experi-
ments were added (bringing the total to 82) and a subset were
additionally analysed for the time course of their activations, again
in relation to the theory and supporting experiments. Relevant to
present concerns is the finding regarding the stage that Indefrey
and Levelt call ‘conceptually driven lemma access’. In both the
2000 and 2004 analyses, the only region to show the activation
pattern consistent with conceptually driven lemma access (a pat-
tern defined by activation in picture naming and word generation
and not word reading) was the mid-part of the left middle tem-
poral gyrus. Invoking evidence from the time course of this
Figure 7 (A) CT scan demonstrating infarction of the left
anterior and mid temporal lobe, insula and inferior frontal
lobe; (B) CT scan demonstrating infarction of the left anterior
temporal lobe and portions of the insula; (C) T1-weighted MRI
scan demonstrating patchy infarction of the left anterior and
mid temporal lobe; (D) T1-weighted MRI scan demonstrating
extensive infarction of the left anterior, mid and portions of
the posterior temporal lobe as well as the left insula.
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Figure 9 Maps of the reliability (t-statistic) of the difference in SemErr, after factoring out total lesion volume, between patients with
and without lesions in each voxel (rendered on the MNI-space ch2 volume). Voxels exceeding an uncorrected t-test threshold are
rendered in a red (t = 1.67) to yellow (t44) scale, while non-significant values are rendered on a scale from green (t just below
threshold) to blue (t = 0 or below). Maps (A—D) are at MNI x coordinates of �60, �54, �48 and �42, respectively. Map (E) is a single
axial slice at z =�27.
Figure 8 Maps depicting, for each voxel, the difference between the proportion of patients with lesions in the high-SemErr group
(20 highest SemErr scores) and the proportion of patients with lesions in the low-SemErr group (18 lowest SemErr scores). Differences
of 0.4 (reflecting differences of 7–8 patients) are plotted in red, while differences of 0.6 or more are plotted in yellow. Maps (A—D) are
at MNI x coordinates of �60, �54, �48 and �42, respectively. Map (E) is a single axial slice at z =�27.
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activation, the authors argued that this area was more likely to be
involved with lemma selection than with the preceding stages of
conceptual processing (Fig. 1) (Indefrey and Levelt, 2004).
As stated in the Introduction section, lemma selection is the
stage at which semantic errors arise during production.
According to the interactive two-step model—a model that was
developed and extensively studied by members of our team and
that is geared to explaining the genesis of production errors in
unimpaired and impaired speakers (Dell and O’Seaghdha, 1991;
Dell et al., 1997; Foygel and Dell, 2000; Schwartz et al., 2006)—
semantic errors arise when the wrong lemma is selected and the
corresponding phonological form is correctly retrieved and spoken
(Fig. 1; right panel). The many-to-one mapping between semantic
features and lemmas creates the preconditions for semantic errors;
when the target’s semantic features are activated, this activation
passes to the target lemma and, to a lesser degree, lemmas that
share its features. Because lemma selection is probabilistic, seman-
tic competitors occasionally win out even in healthy speakers.
A computational case-series analysis of 94 individuals with aphasia
showed that the heightened probability of semantic and other
whole-word substitution errors could be quantitatively modelled
by reducing the weights on connections between semantic
features and lemmas (Schwartz et al., 2006). In this theoretical
context, our findings identify the mid-part of the middle temporal
gyrus as an essential component of the neural instantiation of
lemma selection.
This conclusion is compatible with other accounts of how the
left anterior temporal lobe functions in semantic word production.
For example, in the convergence zone framework developed by
the Iowa group, higher order cortices, including temporal pole and
inferotemporal regions, play an intermediate or mediational role in
concept and word retrieval. These ‘convergence regions’ contain
systems that promote the reinstatement of elemental representa-
tions of concepts or words in distributed brain regions, via recur-
rent connections (Damasio, 1989; Damasio and Damasio, 1994;
Damasio et al., 2004). In applying this framework to their seminal
PET/lesion findings on category-specific naming deficits, Damasio
and colleagues argued that left temporal pole and inferotemporal
cortices ‘hold knowledge about how to reconstruct a certain pat-
tern (for example, the phonemic structure of a given word) within
the appropriate sensorimotor structures’ (Damasio et al., 1996
p. 504). Here and elsewhere (e.g. Damasio and Damasio, 1992),
they drew an explicit connection between the function ascribed to
left temporal pole and inferotemporal cortices in convergence
zone theory and the processing step in psycholinguistic theory
that intervenes between concepts and word forms. That process-
ing step is lemma selection and it is noteworthy that the area we
identified as critical for semantic error production is fully contained
within their temporal pole and inferotemporal area (Damasio and
Damasio, 1992; Damasio et al., 1996; Fig. 1A).
The left anterior temporal lobe appears to be particularly impor-
tant for conceptualizing and/or naming famous people and other
unique entities (Damasio et al., 1996; Tranel et al., 1997; Gorno
Tempini et al., 1998; Gorno Tempini and Price, 2001; Tranel,
2006), perhaps because these rostral brain areas subserve a
finer, more specific level of conceptualization of persons or
things (Damasio and Damasio, 1994; Gorno Tempini and Price,
2001; Martin and Chao, 2001; Patterson et al., 2007). Might
left anterior temporal lobe damage then predispose to semantic
naming errors by blurring close conceptual distinctions? Our evi-
dence suggests not. Each of the comprehension tests that entered
into the composite scores (Vcomp and NVcomp) required concep-
tualization of close semantic distinctions, so in factoring out these
scores we controlled for this possibility. Moreover, the lesson from
semantic dementia is that it is only with bilateral anterior temporal
lobe damage that one begins to see the conceptual over- and
under-generalization indicative of conceptual blurring (Lambon
Ralph et al., 2001b; Patterson et al., 2007; Lambon Ralph and
Patterson, 2008). Given this, and based on the fact that our ante-
rior temporal lobe effect survived the filtering of conceptualization
measures, a more likely possibility is that the left anterior temporal
lobe is specialized for conveying fine-grained distinctions to the
lexical system. In the interactive two-step model, these distinctions
are sent to potential lemma units by the semantic-to-lemma con-
nections. The weights of these connections (s-weights in Fig. 1)
are set by a learning algorithm that emphasizes or de-emphasizes
the contribution of certain features to lemma selection (Gordon
and Dell, 2003; Oppenheim et al., in press). We suggest that
damage to the left anterior temporal lobe blunts this finer grain
of differentiation, thereby raising the probability of semantic
errors.
It must be emphasized that the region within the left anterior
temporal lobe that our analysis picked out almost certainly under-
estimates the extent of the temporal lobe involvement. As noted
earlier, there were too few patients with lesions in the inferior
temporal or fusiform gyri to detect effects there. Given the
many studies reporting activation in these inferotemporal areas
during semantic and/or lexical processing (Damasio et al., 1996),
it is not unlikely that an association with SemErr would have been
found there, at least in the unfiltered analysis. The weak coverage
in these areas is a consequence of vascular anatomy and a well-
known limitation of lesion mapping in post-stroke aphasia. A few
studies have circumvented this problem by enrolling patients with
other focal aetiologies (Damasio et al., 1996) or using functional
neuroimaging to identify effects remote from the lesion (Sharp
et al., 2004; Crinion and Price, 2005).
Beyond the anterior temporal lobeExcept for the inferior temporal/fusiform gyri, coverage in our
study was good for left peri- and extra-sylvian regions previously
implicated in semantic word processing, and we did identify areas
outside of the anterior temporal lobe that correlated with semantic
error rates. One was a posterior temporal region located in the
lateral superior sector of Brodmann area 37; the other was in lat-
eral prefrontal cortex encompassing parts of the inferior and
middle frontal gyri (Brodmann area 45/46). In both areas, voxels
surpassed the critical threshold in the unfiltered analysis and in the
analysis that filtered out Vcomp. However, filtering out NVcomp
weakened effects here to the degree that no voxels in either area
reached significance. NVcomp is a stringent measure of conceptual
processing. The tests that comprise this measure—Pyramids and
Palms and Camel and Cactus—require concept identification
(what target and foils depict), extraction of relevant features
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(how target and foil are related) and comparison in working
memory (which thematic relations are stronger). Accordingly, per-
formance on these tests has proven sensitive to semantic control
deficits as well as semantic representational deficits (Jefferies and
Lambon Ralph, 2006; Noonan et al., in press).
The tests of the Vcomp composite involve a simple match on
the basis of common reference or synonomy and so are less
demanding of semantic control. Our interest in Vcomp rested on
the possibility of identifying areas in which regressing out Vcomp,
and not NVcomp, eliminated the association with SemErr. This
would have indicated that the effect in these areas was due to
variance shared between accessing words from concepts (in pro-
duction) and accessing concepts from words (in comprehension).
We did not identify any voxels that fitted this pattern. Given this,
we interpret the results of the filtering analyses as evidence that a
sizeable portion of the semantic naming errors generated from
lateral superior Brodmann areas 37 and 45/46 originated in
processes shared between picture naming and the non-verbal
comprehension tests, namely retrieving and/or controlling
semantic information during selection of the lexical concept.
The area we identified in lateral superior Brodmann area 37 may
be part of a posterior temporal network serving word-level seman-
tic processing (Hart and Gordon, 1990) and/or auditory sentence
comprehension (Dronkers et al., 2004). Lack of coverage inferior
to this area may explain why we did not confirm prior evidence
that Brodmann area 37 plays a necessary role in concept-word
mapping in production specifically (Raymer et al., 1997; Foundas
et al., 1998; Hillis et al., 2001a; DeLeon et al., 2007; Cloutman
et al., 2009). That finding could hinge on involvement of the basal
temporal language area (Luders et al., 1986). Among other things,
the basal temporal language area seems to be important for
converting visual-semantic information to phonological representa-
tions (Luders et al., 1991; Usui et al., 2003; Trebuchon-Da
Fonseca et al., 2009). Blocked access to target phonology has
been identified as a possible basis for semantic error production
in naming (Caramazza and Hillis, 1990; Hillis et al., 2001a).
There is also precedent in the literature for the association we
found between semantic error production and damage in lateral
prefrontal cortex. In their study with acute stroke patients,
Cloutman et al. (2009) reported a voxel-based analysis of
damage associated with the S+/� pattern (frequent naming
errors with spared comprehension), which showed effects in
voxels in Brodmann areas 44 and 46. In the primary analysis, in
which multiple regions of interest were entered as predictor
variables in a regression model, only Brodmann area 37 was
significant; Brodmann areas 44 and 45 were entered but did not
contribute.
It has also been shown that lesions in dorsal Brodmann area
44 create vulnerability to experimentally generated semantic inter-
ference in lexical access, manifesting in semantic errors
(Schnur et al., 2006, 2009). That finding adds to the evidence
that inferior prefrontal cortex is important for controlled semantic
retrieval and/or competitive selection during word production
(Bookheimer, 2002; Kan and Thompson-Schill, 2004; Moss
et al., 2005; Thompson-Schill et al., 2005; Badre and Wagner,
2007).
Prefrontal areas outside the well-studied inferior complex
(Brodmann area 44/45/47) may also play an important role in
semantic word production. Recent evidence to this effect comes
from an electrostimulation study that mapped the anatomy of
semantic naming errors in patients undergoing surgical resection
for low-grade dominant hemisphere glioma (Duffau et al., 2005).
Seventeen (of 150) surgical patients produced such errors during
stimulation mapping, most of whom had tumours located in the
dominant temporal or frontal lobes. ‘Semantic sites’ (i.e. sites that
yielded semantic naming errors during the stimulation) were
mapped in cortex that bordered the tumour and in the fibre
tracts exposed by the resections. Within the temporal lobe,
semantic sites were found in the posterior part of the cortex
surrounding the superior temporal sulcus and in white matter
tracts deep to the sulcus, extending anteriorly. Within the frontal
lobe, semantic sites were identified in the lateral orbito-frontal
region and in the part of the medial frontal gyrus anterior to
the dorsal pre-motor language area previously identified by
Duffau and colleagues (Duffau et al., 2003).
The prefrontal voxel cluster we identified occupied part of the
medial frontal gyrus corresponding to Brodmann area 46, along
with Brodman area 45, which has a well-known association with
semantic processing and competitive selection (for reviews see
Bookheimer, 2002 and Devlin and Watkins, 2007). Medial frontal
gyrus is considered part of the dorso-lateral prefrontal cortex,
which is important for a variety of executive functions.
Functional neuroimaging evidence implicates left middle frontal
gyrus in verbal working memory (Smith et al., 1996) and men-
tal search during lexical retrieval (Grabowski et al., 1998). This
may explain why the association between middle frontal
gyrus and semantic naming errors that we saw in the unfiltered
analysis was statistically eliminated by the filtering of NVcomp.
That is, it could be that damage to middle frontal gyrus generates
semantic errors in naming by compromising mental search for the
precise lexical concept, a process that is shared with NVcomp. This
would bring our finding in line with the thesis that multi-modal
semantic deficits in aphasia, unlike those in semantic dementia,
have their basis in regulatory/executive processes supported by
the frontal lobes (Jefferies and Lambon Ralph, 2006; Jefferies
et al., 2007; Noonan et al., in press).
Negative findings for posterior superiortemporal gyrusThere is substantial evidence in both the neuroimaging and
aphasia literature that cortices surrounding the posterior third of
the left superior temporal sulcus, occupying posterior superior and
middle temporal gyri (posterior superior temporal gyrus/middle
temporal gyrus), participate in the brain system for multimodality
comprehension (e.g. Hart and Gordon, 1990; Vandenberghe
et al., 1996; Booth et al., 2002; Saygin et al., 2003). In agreement
with this, studies by Hillis’ group and by Duffau et al. (2005)
identified semantic sites in this posterior superior temporal
gyrus/middle temporal gyrus area. We did not. While there
were significant and near-significant voxels throughout middle
temporal gyrus, the clusters were located anterior to posterior
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superior temporal gyrus/middle temporal gyrus (in mid to anterior
middle temporal gyrus) or posterior to it (in lateral superior
Brodmann area 37). The superior temporal gyrus, including
Wernicke’s area in the posterior third, yielded low t-values in
both unfiltered and filtered analyses. It is evident from the cover-
age map (Fig. 2), that many patients had lesions in this area, so
there was adequate power to detect effects here.
Doubts about the localization of semantic processing to poster-
ior superior temporal gyrus (Wernicke’s area) have been raised
before, in the neuroimaging literature (Binder et al., 2009) and
in lesion mapping studies in aphasia. For example, Dronkers et al.
(2004) mapped lesions associated with auditory sentence compre-
hension scores in 64 chronic aphasic patients on a voxel-wise
basis. Significant effects were found in five left hemisphere
regions: posterior middle temporal gyrus (Brodmann area
21/37), anterior Brodmann area 22, superior temporal sulcus/
Brodmann areas 39, 46 and 47. Notably, posterior Brodmann
area 22 did not contribute (see also Bates et al., 2003).
In the words of Dronkers et al. (2004), the classical association
of Wernicke’s area with language comprehension may be ‘epiphe-
nomenal’ and due rather to the involvement of distinct
surrounding areas.
It is conceivable that the strong and specific associations that
the Hopkins group (Hillis et al., 2001a) found between compre-
hension errors and the Brodmann area 22 region of interest
reflected the contribution of anterior Brodmann area 22 more
than Wernicke’s area in the posterior sector. Alternatively, their
Brodmann area 22 effect could be revealing something specific to
acute aphasia. It is well-known that patients who present acutely
with Wernicke’s aphasia sometimes show rapid resolution of the
neologistic jargon and profound comprehension deficit that define
this syndrome (Kertesz and Benson, 1970; Goodglass and Kaplan,
1983; Benson and Ardila; 1996). A functional neuroimaging study
of speech comprehension in patients with chronic, left temporal
lobe damage found right temporal lobe activation in these patients
within the normal range; moreover, this activation was correlated
with auditory comprehension scores outside the magnet (Crinion
and Price, 2005; and for related evidence, see also Sharp et al.,
2004 and Crinion et al., 2006). The implication is that in chronic
patients, variation in auditory comprehension scores can be
explained at least in part by the right hemisphere’s capacity to
sustain performance. In acute patients, tissue damage in posterior
superior temporal gyrus could temporarily impede or suppress this
residual capacity (e.g. through diaschisis), thereby yielding the cor-
relation with performance that Hillis and colleagues observed.
ConclusionsThis study presents the first definitive evidence for a causal relation
between left anterior temporal lobe lesions and semantic errors in
lexical access. Our results line up nicely with evidence from
semantic dementia, convergence zone theory and meta-analyses
of neuroimaging studies of word production. Drawing on the
interactive two-step model of lexical access in naming, we suggest
that the left anterior temporal lobe is specialized for conveying
fine-grained semantic distinctions to the lexical system, at the
level of abstract, pre-phonological word forms (lemmas). Focal
damage to left anterior temporal lobe blunts this finer grain of
differentiation, increasing the competition between the target
word and its semantic neighbours and raising the probability
that the competitor will be erroneously selected and realized
in output.
AcknowledgementsWe are very grateful to the research participants and caregivers
who made this study possible. We also wish to acknowledge the
many research assistants and associates who helped with recruit-
ment, testing and scoring, including Laura Barde, Laurel Brehm,
Jacqueline Cairone, Krista Cullen, Jennifer Gallagher, Marisa
Gauger, A. Cris Hamilton, Jesse Hochstadt, Rachel Jacobson,
Laura MacMullen, Michelle Rapp and Paula Sobel. Finally, we
wish to thank Argye Hillis, Daniel Tranel and an anonymous
reviewer for their careful reading and helpful suggestions on an
earlier draft.
FundingNational Institutes of Health (RO1 DC000191 to M.F.S., R01
MH073529 to D.Y.K.).
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