voxel-based lesion-symptom mapping evidence from aphasia - brain

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

3412 | Brain 2009: 132; 3411–3427 M. F. Schwartz et al.

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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.

Semantic word retrieval Brain 2009: 132; 3411–3427 | 3413

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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


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


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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