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https://doi.org/10.1177/0963721418787486
Current Directions in Psychological
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DOI: 10.1177/0963721418787486
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PSYCHOLOGICAL SCIENCE
For more than 150 years, associations between the loca-
tion of brain damage and cognitive deficits have shed
light on the brain systems that are critical for human
cognition and behavior. Contemporary advances in
noninvasive neuroimaging methods and sophisticated
analysis techniques have made it possible to apply this
classic lesion method on a much finer anatomical scale.
Recent research on the neural basis of language using
large data sets from individuals with language deficits
after stroke (aphasia) has provided new insights into
the functional neuroanatomy of the human language
system by identifying the cognitive subsystems that sup-
port language processing and the neural basis of those
subsystems.
The modern version of the lesion method is called
lesion-symptom mapping (Bates etal., 2003; Rorden &
Karnath, 2004) and uses brain scans (MRI or computed
tomography) to localize brain damage (lesions) for each
individual in a group of participants with varying
degrees of deficit. For each small patch of the brain (a
voxel; typically a 1-mm × 1-mm × 1-mm or 3-mm ×
3-mm × 3-mm cube), a lesion-symptom mapping
analysis tests whether people with damage in that patch
have a more severe deficit than people who do not
have damage in that patch (because their brain damage
is in a different location). Lesion-symptom mapping has
been used to examine a wide variety of different cogni-
tive or behavioral symptoms on the basis of one of
three broad approaches. The clinical approach com-
pares participants with different clinical diagnoses, for
example, Broca’s aphasia versus Wernicke’s aphasia.
The theoretical approach compares performance on a
test or measure that is relevant to a particular theory
or hypothesis. For example, a theory about the process-
ing steps involved in word production might identify
speech sound errors, such as saying “girappe” instead
of “giraffe,” as a key symptom to use for lesion-symptom
mapping. The newest approach defines symptoms in a
787486CDPXXX10.1177/0963721418787486Mirman, ThyeCore Language Systems and Lesion-Symptom Mapping
research-article2018
Corresponding Author:
Daniel Mirman, University of Alabama at Birmingham, Department
of Psychology, CH 415, 1300 University Boulevard, Birmingham, AL
35294
E-mail: dan@danmirman.org
Uncovering the Neuroanatomy of
Core Language Systems Using
Lesion-Symptom Mapping
Daniel Mirman and Melissa Thye
Department of Psychology, University of Alabama at Birmingham
Abstract
Recent studies have integrated noninvasive brain-imaging methods and advanced analysis techniques to study
associations between the location of brain damage and cognitive deficits. By applying data-driven analysis methods
to large sets of data on language deficits after stroke (aphasia), these studies have identified the cognitive systems
that support language processing—phonology, semantics, fluency, and executive functioning—and their neural basis.
Phonological processing is supported by dual pathways around the Sylvian fissure, a ventral speech-recognition
component and a dorsal speech-production component; fluent sentence-level speech production relies on a more
anterior frontal component, and the semantic system relies on a hub in the anterior temporal lobe and frontotemporal
white-matter tracts. The executive function system was less consistently localized, possibly because of the kinds of
brain damage tested in these studies. This review synthesizes the results of these studies, showing how they converge
with contemporary models of primary systems that support perception, action, and conceptual knowledge across
domains, and highlights some divergent findings and directions for future research.
Keywords
language, speech, aphasia, neuroimaging, neuropsychology
2 Mirman, Thye
data-driven way—researchers start with many different
measures of cognitive performance and use a statistical
method that combines highly correlated measures to
identify a few scores that capture most of the variability
across all of the measures and likely reflect the underly-
ing cognitive systems. The long-term goal is for these
three approaches to converge: to develop cognitive
theories that explain the observed data-driven syn-
drome clusters in ways that are clinically relevant.
Recent studies from four independent research
groups1 using the data-driven approach have identified
four cognitive systems of language processing and their
associated brain regions: phonology, semantics, fluency,
and executive functions. Figure 1 illustrates how differ-
ent language tasks relate to these four identified sys-
tems. Language tasks rarely rely on a single system—even
seemingly simple language tasks, such as naming com-
mon objects, repeating words, or matching words to
pictures, draw on multiple systems. These systems are
complementary and interactive, working together to
perform everyday language tasks. A key unique contri-
bution of the data-driven approach is that performance
on multiple tasks is combined in order to overcome the
limitations of any single task, thus triangulating the
dissociable underlying cognitive systems. For example,
phonological-discrimination, repetition, and picture-
naming tasks each draw on phonology as well as other
systems, but data-driven methods provide a composite
score that reflects the common latent phonology factor
that underlies performance in all of those tasks and
removes the contribution of other systems. The sub-
stantial convergence across different laboratories, each
using a somewhat different battery of tests and testing
different groups of individuals with aphasia, reveals
that this organization of language processing is quite
robust.
Phonology
The first and most thoroughly described system is pho-
nology: the speech sounds that make up words. Pho-
nological processing is the intermediate step between
higher-level language processes, such as sentence- and
word-level processing, and lower-level auditory percep-
tion and articulation of language. Phonological deficits
include difficulty judging whether two syllables are the
Fluency
Word Finding
Speech Fluency
Naming
Reading & Writing
Repetition
Apraxia of Speech
Sentence Production
Sentence Comprehension
Word-to-Picture Matching
Working Memory
Phonological Discrimination
Semantic Matching
General Cognition
Phonological
Production
Phonological
Recognition Semantics
Executive
Functions
Fig. 1. Schematic depiction of the extent to which various language tasks and deficits (rows) rely on four language
systems (columns): fluency, phonology (separated here into phonological production and phonological recogni-
tion), semantics, and executive functions. Saturation of the cells shows the approximate strength of the relationship
between these systems and language tasks or deficits. Detailed results, including specific tests and factor loadings,
are provided in Table S1 at https://osf.io/6gnvb/.
Core Language Systems and Lesion-Symptom Mapping 3
same or different, difficulty judging whether two words
rhyme or not, and difficulty repeating a word or non-
word. Several of the reports also differentiated between
articulation-production of speech sounds and recogni-
tion of speech sounds. This distinction requires using
measures that are specific to deficits in phonological
production or perception rather than measures that
reflect both (e.g., repetition requires both correct per-
ception and production; see Table S1 at https://osf
.io/6gnvb/).
Deficits in the phonological-production subsystem
are associated with damage in a dorsal pathway primar-
ily involving inferior parietal and frontal regions (Fig.
2a, darker green). Deficits in the phonological-
recognition subsystem are associated with damage in
a ventral pathway extending from the posterior to ante-
rior superior temporal lobe (Fig. 2a, lighter green). This
dual-stream architecture of the phonological system
broadly aligns with the contemporary view of the com-
putational neuroanatomy of speech processing (Hickok
& Poeppel, 2007). These two subsystems interact and
work together: The goal of speech production is par-
ticular speech sounds, so the speech-perception system
plays an important role in setting speech-production
targets and monitoring articulation (Hickok, 2012).
When speech perception and production were not dis-
sociated, a general phonological factor was identified
and associated with damage along the superior
temporal sulcus, including Heschl’s gyrus and planum
temporale. This region, where the two phonological
subsystems come into contact, may be critical for the
auditory-motor transformations involved in setting audi-
tory targets and monitoring speech programs.
Semantics
The second system is semantics: conceptual knowledge
about objects and the meanings of words. Semantic
knowledge about an object includes what that object
looks and sounds like, how it acts and how we act on
or with it, the emotions it evokes in us, and so forth.
This knowledge is distributed across modality-specific
perceptual, motor, and emotional systems and inte-
grated in convergence zones or hubs (Barsalou, Simmons,
Barbey, & Wilson, 2003; Meyer & Damasio, 2009; Rogers
etal., 2004). Semantic deficits are most often exhibited
in comprehension tasks such as matching words or sen-
tences to pictures or judging whether two words are
synonyms, but they can also affect nonverbal tasks such
as matching related pictures (e.g., whether a pyramid
goes with a palm tree or a pine tree).
Deficits in the semantic system are associated with
anterior temporal lobe damage (Fig. 2a, yellow), includ-
ing the temporal pole and the midanterior portions of
the middle and superior temporal gyri. For comprehen-
sion tasks, this is the continuation of the ventral stream:
ab
Fig. 2. Brain regions critical for language processing. The neuroanatomy of core language systems (a) is shown on the lateral surface of
the left hemisphere: semantics (yellow), fluency (red), and phonology (green; lighter green shows the phonological recognition system,
whereas darker green shows the phonological production system; the two systems overlap in the posterior portions of the Sylvian fissure
and the superior temporal gyrus). White-matter tracts particularly important for language processing are shown in (b): arcuate fasciculus
(red), inferior fronto-occipital fasciculus (green), and uncinate fasciculus (blue). A more detailed description of the brain regions involved
in each system, including peak voxel coordinates, is provided in Table S2 at https://osf.io/6gnvb/.
4 Mirman, Thye
The progression from posterior to anterior temporal
regions corresponds to the progression from recogniz-
ing speech sounds and syllables to recognizing words
and comprehending what those words mean (see also
DeWitt & Rauschecker, 2012). This progression aligns
with extensive evidence that the anterior temporal lobe
is a critical hub for integrating semantic knowledge
that is distributed across modality-specific systems
(Binder & Desai, 2011; Lambon Ralph, Jefferies,
Patterson, & Rogers, 2017; Patterson, Nestor, & Rogers,
2007).
Poststroke semantic deficits are also associated with
damage to white-matter pathways, particularly the infe-
rior fronto-occipital fasciculus and the uncinate fascicu-
lus (Fig. 2b), which appear to be critical for effective
functioning of the distributed semantic system. Damage
to white-matter bottlenecks—regions where multiple
tracts come together—is particularly disruptive (Griffis,
Nenert, Allendorfer, & Szaflarski, 2017; Mirman, Chen,
etal., 2015; Mirman, Zhang, Wang, Coslett, & Schwartz,
2015). This is, presumably, because these bottlenecks
are locations where even a small amount of damage
can disrupt multiple tracts, producing broad semantic
network dysfunction (for a computational approach see
L. Chen, Lambon Ralph, & Rogers, 2017).
Fluency
The third system, which was identified in a subset of
the studies, is fluency: the ability to produce connected
speech rapidly and smoothly. This requires rapid coor-
dination of multiple processes (e.g., Nozari & Faroqi-Shah,
2017; Wilson et al., 2010), including articulatory-
phonological planning and execution, which corre-
sponds to the phonological production system discussed
above, and retrieving words to fit the semantic and
syntactic constraints, which corresponds to the seman-
tic system discussed above (see also Q. Chen, Middleton,
& Mirman, 2018). The data-driven studies identified a
distinct fluency system that corresponds to planning
and structuring sentences, including syntax and work-
ing or short-term memory. People with deficits in this
sentence-level planning and structuring system tend to
produce shorter utterances, slower speech (fewer words
per minute), and less grammatically complex sentences
(e.g., fewer embedded clauses). They also tend to make
more syntactic omissions, such as omitting closed class
words (determiners, prepositions, etc.) or producing
incomplete sentences. However, sentence-comprehension
deficits are not strongly associated with this system, sug-
gesting that it is more closely related to sentence-level
planning and execution rather than general syntactic
processing (see also Linebarger, Schwartz, & Saffran,
1983; Thothathiri, Kimberg, & Schwartz, 2012).
Fluency deficits are associated with damage in the
insula and inferior frontal and precentral areas (Fig. 2a,
red). This is anterior to the region associated with pho-
nological production deficits and may reflect a higher
level of speech-production planning and coordination,
that is, sentence- or utterance-level planning rather than
word- or syllable-level planning or articulation of indi-
vidual speech sounds. Related research also suggests
that fluency deficits are associated with middle frontal
gyrus damage and frontal white-matter damage
(Basilakos etal., 2014; Catani etal., 2013; Fridriksson,
Guo, Fillmore, Holland, & Rorden, 2013; Rogalski etal.,
2011; Wilson etal., 2010), including the anterior seg-
ment of the arcuate fasciculus (Fig. 2b, red) and the
frontal aslant tract, which connects superior and inferior
portions of the frontal lobe. The frontoparietal speech-
production system is neuroanatomically similar to the
frontoparietal system for the production of skilled tool-use
actions (e.g., Brandi, Wohlschlager, Sorg, & Hermsdorfer,
2014; Johnson-Frey, 2004). Anterior-to-posterior progres-
sion within the speech-production planning system aligns
with general theories about the hierarchical organization
of frontal planning systems (Botvinick, 2008) in which
more anterior regions are responsible for higher-level
planning and maintenance of tasks and goals (e.g., making
a cup of coffee, or producing a full sentence or narrative)
and more posterior regions are responsible for lower-level
planning of individual actions involved in accomplishing
those tasks or goals (e.g., scooping coffee grounds into
the coffee maker or sugar into the cup, or saying a single
word).
Executive Functions
The final factor, which was identified in a subset of the
studies, is executive functioning: cognitive abilities,
such as planning, reasoning, cognitive flexibility, cogni-
tive control, and selective attention (e.g., Jurado &
Rosselli, 2007), that are not specific to language but are
important for language processing. Lacey, Skipper-
Kallal, Xing, Fama, and Turkeltaub (2017) reported that
executive function deficits were associated with damage
in middle frontal gyrus (dorsolateral prefrontal cortex)
and posterior frontal white matter, but the other studies
did not converge on a consistent lesion correlate. This
may be because (a) some of the regions that are critical
to executive functions are outside the territory of the
middle cerebral artery, so they are unlikely to be dam-
aged by strokes that affect language processing, and
(b) the executive function system is distributed across
both hemispheres of the brain, so damage within the
left hemisphere may not be sufficiently strongly associ-
ated with executive function deficits. Thus, executive
functions may be impaired in poststroke aphasia, but
Core Language Systems and Lesion-Symptom Mapping 5
the relationship between executive functions and lan-
guage remains unclear (see also Fedorenko, 2014).
Conclusions and Future Directions
Two systems form the core of spoken language: pho-
nology and semantics. The phonological system has
two subcomponents organized in dual pathways around
the Sylvian fissure, a dorsal-stream production compo-
nent and a ventral-stream recognition component,
which is consistent with contemporary dual-stream
models of speech recognition and production (Hickok,
2012; Hickok & Poeppel, 2007). The semantic system
is broadly distributed and relies on a hub in the anterior
temporal lobe and frontotemporal white-matter tracts,
which is consistent with the current theories and neu-
rocomputational models of semantic cognition (Lambon
Ralph etal., 2017). Fluent speech-production deficits
are primarily associated with frontal damage, anterior
to the regions where damage is associated with single-
word phonological-production deficits. This suggests
that language production may rely on the same com-
putational and neural systems that support other kinds
of hierarchical, sequential action planning and execu-
tion (Botvinick, 2008; Hickok, 2012; see also Weiss
etal., 2016). A key overarching theme is that language
processing is situated in the context of primary systems
that support perception, action, and conceptual knowl-
edge across domains.
Two psycholinguistic constructs have not emerged in
these data-driven lesion-symptom mapping studies. One
is the lexicon—a mental inventory of words. It may be
that the lexicon is an emergent property of interactions
between phonological and semantic systems rather than
a discrete system. The other is syntax—knowledge of a
language’s grammatical rules. Although often associated
with nonfluent aphasia, fluency (rapid and smooth pro-
duction of connected speech) is neither necessary nor
sufficient for syntax. In fact, substantial syntax knowl-
edge is preserved in so-called agrammatism (e.g., Line-
barger etal., 1983), and syntax deficits in aphasia may
be a result of a more general reduction in cognitive
resources (e.g., Caplan, Michaud, & Hufford, 2013).
The clinical relevance of data-driven lesion-symptom
mapping studies has yet to be established, although there
is potential for improved diagnosis, stronger integration
with neural systems, and clearer guidance for treatment
selection. Ideally, a new data-driven classification system
would make contact with the outcome that is of utmost
importance for people with aphasia and for their clini-
cians—functional communication—but it is not yet clear
how these systems relate to the ability to communicate
in real-world settings. In addition, data-driven methods
are necessarily limited by the data that are driving them.
Pragmatic aspects of language and communication were
not assessed in any of these studies, and fluency, written
language, and executive functions were only minimally
assessed, and not in all of the studies.
Finally, lesion-symptom mapping methods are con-
tinuing to develop and evolve. A new generation of
multivariate methods captures the contributions of mul-
tiple brain regions more effectively (Pustina, Avants,
Faseyitan, Medaglia, & Coslett, 2018; Zhang, Kimberg,
Coslett, Schwartz, & Wang, 2014). Updating the classic
lesion method, contemporary lesion-symptom mapping
leverages advanced neuroimaging, statistical, and
machine-learning techniques to identify brain regions
that are critical for specific cognitive functions. Data-
driven lesion-symptom mapping has recently revealed
the core systems that support language function and
their neural basis and informed theoretical accounts of
language in ways that are clinically meaningful.
Recommended Reading
Dronkers, N. F., Ivaonova, M. V., & Baldo, J. V. (2017). What
do language disorders reveal about brain-language rela-
tionships? From classic models to network approaches.
Journal of the International Neuropsychological Society,
23, 741–754. A review of how language disorders have
elucidated the neural basis of language.
Hickok, G., & Small, S. L. (Eds.). (2016). Neurobiology of lan-
guage. San Diego, CA: Academic Press. An encyclopedic
collection of short chapters covering the neurobiology
of language.
Karnath, H.-O., Sperber, C., & Rorden, C. (2018). Mapping
human brain lesions and their functional consequences.
NeuroImage, 165, 180–189. doi:10.1016/j.neuroim
age.2017.10.028. A somewhat more technical overview of
lesion-symptom mapping.
Mesulam, M.-M., Rogalski, E. J., Wieneke, C., Hurley, R. S.,
Geula, C., Bigio, E. H., . . . Weintraub, S. (2014). Primary
progressive aphasia and the evolving neurology of the
language network. Nature Reviews Neurology, 10, 554–
569. doi:10.1038/nrneurol.2014.159. A review of progres-
sive language deficits that result from neurodegenerative
diseases (primary progressive aphasia), which offers a
different perspective on the neuroanatomy of language.
Wilson, S. M. (2017). Lesion-symptom mapping in the study
of spoken language understanding. Language, Cognition
and Neuroscience, 32, 891–899. doi:10.1080/23273798.201
6.1248984. An accessible overview of the lesion-symptom
mapping method.
Action Editor
Randall W. Engle served as action editor for this article.
ORCID iDs
Daniel Mirman https://orcid.org/0000-0001-5472-0220
Melissa Thye https://orcid.org/0000-0002-6383-6361
6 Mirman, Thye
Acknowledgments
A preliminary version of this summary was presented at the
2016 American Speech-Language-Hearing Association Con-
vention (Philadelphia, Pennsylvania). We thank Peter Turkel-
taub, Ajay Halai, and Matthew Lambon Ralph for providing
the statistical maps of their results, which allowed us to more
accurately describe their findings.
Declaration of Conflicting Interests
The author(s) declared that there were no conflicts of interest
with respect to the authorship or the publication of this
article.
Funding
Preparation of this article was supported by the University of
Alabama at Birmingham.
Note
1. The four research groups were based in Philadelphia,
Pennsylvania (Mirman, Chen, etal., 2015; Mirman, Zhang, etal.,
2015); Columbia and Charleston, South Carolina (Fridriksson
et al., 2016, 2018); Washington, DC (Lacey etal., 2017); and
Manchester, England (Butler, Lambon Ralph, & Woollams, 2014;
Halai, Woollams, & Lambon Ralph, 2017).
References
Barsalou, L. W., Simmons, W. K., Barbey, A. K., & Wilson, C. D.
(2003). Grounding conceptual knowledge in modality-
specific systems. Trends in Cognitive Sciences, 7, 84–91.
Basilakos, A., Fillmore, P. T., Rorden, C., Guo, D., Bonilha,
L., & Fridriksson, J. (2014). Regional white matter dam-
age predicts speech fluency in chronic post-stroke apha-
sia. Frontiers in Human Neuroscience, 8, Article 845.
doi:10.3389/fnhum.2014.00845
Bates, E., Wilson, S. M., Saygin, A. P., Dick, F., Sereno, M. I.,
Knight, R. T., & Dronkers, N. F. (2003). Voxel-based
lesion-symptom mapping. Nature Neuroscience, 6, 448–
450. doi:10.1038/nn1050
Binder, J. R., & Desai, R. H. (2011). The neurobiology of
semantic memory. Trends in Cognitive Sciences, 15, 527–
536. doi:10.1016/j.tics.2011.10.001
Botvinick, M. M. (2008). Hierarchical models of behavior
and prefrontal function. Trends in Cognitive Sciences, 12,
201–208. doi:10.1016/j.tics.2008.02.009
Brandi, M.-L., Wohlschlager, A., Sorg, C., & Hermsdorfer, J.
(2014). The neural correlates of planning and executing
actual tool use. The Journal of Neuroscience, 34, 13183–
13194. doi:10.1523/JNEUROSCI.0597-14.2014
Butler, R. A., Lambon Ralph, M. A., & Woollams, A. M. (2014).
Capturing multidimensionality in stroke aphasia: Mapping
principal behavioral component to neural structures.
Brain, 137, 3248–3266.
Caplan, D., Michaud, J., & Hufford, R. (2013). Dissociations
and associations of performance in syntactic compre-
hension in aphasia and their implications for the nature
of aphasic deficits. Brain & Language, 127, 21–33.
doi:10.1016/j.bandl.2013.07.007
Catani, M., Mesulam, M. M., Jakobsen, E., Malik, F., Martersteck,
A., Wieneke, C., . . . Rogalski, E. J. (2013). A novel frontal
pathway underlies verbal fluency in primary progressive
aphasia. Brain, 136, 2619–2628. doi:10.1093/brain/awt163
Chen, L., Lambon Ralph, M. A., & Rogers, T. T. (2017). A
unified model of human semantic knowledge and its
disorders. Nature Human Behaviour, 1, Article 0039.
doi:10.1038/s41562-016-0039
Chen, Q., Middleton, E., & Mirman, D. (2018). Words fail:
Lesion-symptom mapping of errors of omission in post-
stroke aphasia. Journal of Neuropsychology. Advance
online publication. doi:10.1111/jnp.12148
DeWitt, I., & Rauschecker, J. P. (2012). Phoneme and word
recognition in the auditory ventral stream. Proceedings of
the National Academy of Sciences, USA, 109, E505–E514.
doi:10.1073/pnas.1113427109
Fedorenko, E. (2014). The role of domain-general cogni-
tive control in language comprehension. Frontiers in
Psychology, 5, Article 335. doi:10.3389/fpsyg.2014.00335
Fridriksson, J., den Ouden, D.-B., Hillis, A. E., Hickok, G.,
Rorden, C., Basilakos, A., . . . Bonilha, L. (2018). Anatomy
of aphasia revisited. Brain. Advance online publication.
doi:10.1093/brain/awx363
Fridriksson, J., Guo, D., Fillmore, P. T., Holland, A., & Rorden,
C. (2013). Damage to the anterior arcuate fasciculus pre-
dicts non-fluent speech production in aphasia. Brain,
136, 3451–3460. doi:10.1093/brain/awt267
Fridriksson, J., Yourganov, G., Bonilha, L., Basilakos, A.,
Den Ouden, D.-B., & Rorden, C. (2016). Revealing the
dual streams of speech processing. Proceedings of the
National Academy of Sciences, USA, 113, 15108–15113.
doi:10.1073/pnas.1614038114
Griffis, J. C., Nenert, R., Allendorfer, J. B., & Szaflarski, J. P.
(2017). Damage to white matter bottlenecks contrib-
utes to language impairments after left hemispheric
stroke. NeuroImage: Clinical, 14, 552–565. doi:10.1016/j
.nicl.2017.02.019
Halai, A. D., Woollams, A. M., & Lambon Ralph, M. A.
(2017). Using principal component analysis to capture
individual differences within a unified neuropsychologi-
cal model of chronic post-stroke aphasia: Revealing the
unique neural correlates of speech fluency, phonology
and semantics. Cortex, 86, 275–289. doi:10.1016/j.cortex
.2016.04.016
Hickok, G. S. (2012). Computational neuroanatomy of speech
production. Nature Reviews Neuroscience, 13, 135–145.
doi:10.1038/nrn3158
Hickok, G. S., & Poeppel, D. (2007). The cortical organiza-
tion of speech processing. Nature Reviews Neuroscience,
8, 393–402.
Johnson-Frey, S. H. (2004). The neural bases of complex tool
use in humans. Trends in Cognitive Sciences, 8, 71–78.
doi:10.1016/j.tics.2003.12.002
Jurado, M. B., & Rosselli, M. (2007). The elusive nature of
executive functions: A review of our current understand-
ing. Neuropsychology Review, 17, 213–233. doi:10.1007/
s11065-007-9040-z
Lacey, E. H., Skipper-Kallal, L. M., Xing, S., Fama, M. E.,
& Turkeltaub, P. E. (2017). Mapping common aphasia
Core Language Systems and Lesion-Symptom Mapping 7
assessments to underlying cognitive processes and their
neural substrates. Neurorehabilitation & Neural Repair,
31, 442–450. doi:10.1177/1545968316688797
Lambon Ralph, M. A., Jefferies, E., Patterson, K., & Rogers, T. T.
(2017). The neural and computational bases of seman-
tic cognition. Nature Reviews Neuroscience, 18, 42–55.
doi:10.1038/nrn.2016.150
Linebarger, M. C., Schwartz, M. F., & Saffran, E. M. (1983). Sensitivity
to grammatical structure in so-called agrammatic aphasics.
Cognition, 13, 361–392. doi:10.1016/0010-0277(83)90015-X
Meyer, K., & Damasio, A. (2009). Convergence and diver-
gence in a neural architecture for recognition and mem-
ory. Trends in Neurosciences, 32, 376–382. doi:10.1016/j
.tins.2009.04.002
Mirman, D., Chen, Q., Zhang, Y., Wang, Z., Faseyitan, O. K.,
Coslett, H. B., & Schwartz, M. F. (2015). Neural organization
of spoken language revealed by lesion-symptom
mapping. Nature Communications, 6, Article 6762. doi:10
.1038/ncomms7762
Mirman, D., Zhang, Y., Wang, Z., Coslett, H. B., & Schwartz,
M. F. (2015). The ins and outs of meaning: Behavioral
and neuroanatomical dissociation of semantically-driven
word retrieval and multimodal semantic recognition in
aphasia. Neuropsychologia, 76, 208–219. doi:10.1016/j
.neuropsychologia.2015.02.014
Nozari, N., & Faroqi-Shah, Y. (2017). Investigating the origin
of nonfluency in aphasia: A path modeling approach
to neuropsychology. Cortex, 95, 119–135. doi:10.1016/j
.cortex.2017.08.003
Patterson, K. E., Nestor, P. J., & Rogers, T. T. (2007). Where
do you know what you know? The representation of
semantic knowledge in the human brain. Nature Reviews
Neuroscience, 8, 976–987. doi:10.1038/nrn2277
Pustina, D., Avants, B. B., Faseyitan, O. K., Medaglia, J., &
Coslett, H. B. (2018). Improved accuracy of lesion to
symptom mapping with multivariate sparse canonical cor-
relations. Neuropsychologia, 115, 154–166. doi:10.1016/j
.neuropsychologia.2017.08.027
Rogalski, E. J., Cobia, D., Harrison, T. M., Wieneke, C.,
Thompson, C. K., Weintraub, S., & Mesulam, M.-M. (2011).
Anatomy of language impairments in primary progres-
sive aphasia. The Journal of Neuroscience, 31, 3344–3350.
doi:10.1523/JNEUROSCI.5544-10.2011
Rogers, T. T., Lambon Ralph, M. A., Garrard, P., Bozeat,
S., McClelland, J. L., Hodges, J. R., & Patterson, K. E.
(2004). Structure and deterioration of semantic memory:
A neuropsychological and computational investigation.
Psychological Review, 111, 205–235.
Rorden, C., & Karnath, H.-O. (2004). Using human brain
lesions to infer function: A relic from a past era in the
fMRI age? Nature Reviews Neuroscience, 5, 813–819.
Thothathiri, M., Kimberg, D. Y., & Schwartz, M. F. (2012).
The neural basis of reversible sentence comprehension:
Evidence from voxel-based lesion symptom mapping in
aphasia. Journal of Cognitive Neuroscience, 24, 212–222.
doi:10.1162/jocn_a_00118
Weiss, P. H., Ubben, S. D., Kaesberg, S., Kalbe, E., Kessler,
J., Liebig, T., & Fink, G. R. (2016). Where language meets
meaningful action: A combined behavior and lesion anal-
ysis of aphasia and apraxia. Brain Structure & Function,
221, 563–576. doi:10.1007/s00429-014-0925-3
Wilson, S. M., Henry, M. L., Besbris, M., Ogar, J. M., Dronkers,
N. F., Jarrold, W., . . . Gorno-Tempini, M. L. (2010). Connected
speech production in three variants of primary progres-
sive aphasia. Brain, 133, 2069–2088. doi:10.1093/brain/
awq129
Zhang, Y., Kimberg, D. Y., Coslett, H. B., Schwartz, M. F., &
Wang, Z. (2014). Multivariate lesion-symptom mapping
using support vector regression. Human Brain Mapping,
35, 5861–5876. doi:10.1002/hbm.22590
