Hindawi
Neural Plasticity
Volume 2017, Article ID 2761913, 16 pages
https://doi.org/10.1155/2017/2761913

Research Article
The Cognitive Neuroplasticity of Reading
Recovery following Chronic Stroke:
A Representational Similarity Analysis Approach
Simon Fischer-Baum,1 Ava Jang,1 and David Kajander2
1

Department of Psychology, Rice University, Houston, TX, USA
Department of Psychology, University of Massachusetts, Amherst, MA, USA

2

Correspondence should be addressed to Simon Fischer-Baum; sjf2@rice.edu
Received 7 June 2016; Accepted 12 December 2016; Published 8 February 2017
Academic Editor: Swathi Kiran
Copyright © 2017 Simon Fischer-Baum et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Damage to certain left hemisphere regions leads to reading impairments, at least acutely, though some individuals eventually recover
reading. Previous neuroimaging studies have shown a relationship between reading recovery and increases in contralesional and
perilesional activation during word reading tasks, relative to controls. Questions remain about how to interpret these changes in
activation. Do these changes reflect functional take-over, a reorganization of functions in the damaged brain? Or do they reveal
compensatory masquerade or the use of alternative neural pathways to reading that are available in both patients and controls? We
address these questions by studying a single individual, CH, who has made a partial recovery of reading familiar words following
stroke. We use an fMRI analysis technique, representational similarity analysis (RSA), which allows us to decode cognitive function
from distributed patterns of neural activity. Relative to controls, we find that CH shows a shift from visual to orthographic processing
in contralesional regions, with a marginally significant result in perilesional regions as well. This pattern supports a contralesional
reorganization of orthographic processing following stroke. More generally, these analyses demonstrate how powerful RSA can be
for mapping the neural plasticity of language function.

1. Introduction
A well-articulated network of cortical regions associated
with single word reading has emerged over the past several
decades [1–3], with the left ventral occipitotemporal cortex
[4] and the left angular gyrus [5] identified as two critical
nodes in the reading network. During the acute stage of
stroke, damage to these regions is associated with severe
impairments in the ability to read (e.g., [6–8]). These severe
impairments often resolve during the transition from the
acute to chronic stroke, with many individuals shown to at
least partially recover over the years following damage (e.g.,
[9–15]).
These improvements in reading during the natural recovery from stroke have been argued to provide evidence for
neural plasticity, with the damaged brain reorganizing to

better support the impaired reading functions (e.g., [12, 13,
15, 16]). Neural plasticity could mean a variety of things:
from functional take-over whereby the function previously
performed by a damaged area shifts to a different brain region
to compensatory masquerade, or a refinement of established
but intact cognitive processes to perform a task [17]. Within
the context of reading literature, both of these hypotheses
have been proposed to account for the acute to chronic
improvements following stroke.
According to the functional take-over hypothesis there is
a region in the patient’s brain whose associated function is
different than the corresponding region in the undamaged
population. The function of that region in the patient more
closely matches the function of the damaged region in
the undamaged population. As with much of the language
recovery literature, there is debate over whether the region

2
that takes over the function is contralesional or perilesional
(e.g., [18]). That is, some have argued that recovery of reading
is associated with a retuning of the neural response of the
homologous right hemisphere regions, such that this region
now computes the function normally associated with the
damaged tissue in the left hemisphere (e.g., [12, 16, 19]).
Others have argued that the retuning occurs in the tissue just
surrounding the lesion (e.g., [13, 15]).
Support for the functional take-over hypothesis largely
comes from fMRI studies of reading in the damaged brain.
For example, in unimpaired readers, it is typical for a region of
the left ventral occipitotemporal, frequently referred to as the
visual word form area (VWFA), to be more activated to words
than baseline, with the region’s response being case, font, and
location invariant [4]. This pattern suggests that the region
is involved in processing orthographic information about
written words, that is, abstract information about the letter
identities in the word and their order. When undergoing taskrelated fMRI, patients with damage to the VWFA typically
show greater activation to words compared to baseline in
the right hemisphere homologue of this region (e.g., [13, 15,
16, 19]) and/or in regions just adjacent to the lesion (e.g.,
[13, 15]). On the surface, the fact that patients show increased
activation to words in regions not typically observed in
the unimpaired population suggests that a reorganization
of cognitive functions has occurred. Specifically, the orthographic function of the damaged region is hypothesized to be
reorganized into other regions that do not typically carry out
that function (e.g., [15, 16]).
There are alternative explanations for this reading recovery that do not require assuming functional take-over. Others
have argued that the residual reading ability following damage to these regions is due to the refinement of alternative
neural pathways for word reading that exist even in the
undamaged brain, or a type of compensatory masquerade. For
example, these patients have been argued to rely on the
right hemisphere’s normal capacity for visual word processing
(e.g., [10, 20, 21]) or alternatively on left hemisphere reading
pathways that do not involve the damaged regions (e.g.,
[14]). In these accounts, recovery over time results from
participants learning to more efficiently use these alternative
pathways, rather than a dynamic change in the neural
organization of the reading system. According to the compensatory masquerade hypothesis, even after reading has
partially recovered, the functions in the undamaged regions
of the patient’s brain are the same as the functions in the
corresponding regions in the unimpaired population.
Changes in the location of word versus baseline activation
in patient’s brains may also be consistent with the compensatory masquerade hypothesis. Increases in activation in the
patient could occur even without functional reorganization;
instead they may reflect that the patient is engaging neural
regions whose cognitive functions have not been altered by
brain damage but that are being used in an atypical way for the
task of reading (e.g., [22, 23]). For example, the right hemisphere activation may reflect that the word is being processed
visually, but not orthographically. The same right hemisphere
region may also process visual, but not orthographic, information about written stimuli in the unimpaired brain (e.g.,

Neural Plasticity
[16, 24]). Impaired readers show greater activation in that
region compared to baseline than the control participants
because this visual processing is not well suited for word
reading. Without the orthographic processes in the lvOT,
impaired readers rely more heavily on these visual processes
for word recognition, leading to more activation in the region.
Alternatively, the right hemisphere activation may reflect
engagement of cognitive processes that are not typically
involved in reading for unimpaired readers but become part
of the reading process after damage, processes like cognitive
control, working memory, or response selection.
Support for this compensatory masquerade interpretation
of changes in activation profile comes from several longitudinal studies of individuals with VWFA damage. Early in the
course of recovery, patients show an increase in the activation
of the right VWFA activation to words relative to baseline. As
recovery continues and reading improves, less right VWFA
activation is observed and greater perilesional activation is
observed (e.g., [12, 13]). If, over time, the right VWFA takes
on the functional properties of the damaged left VWFA, the
opposite pattern would be expected, with more activity in the
right VWFA as reading recovers.
This problem of interpreting what the meaning of changes
in activation can tell us about the reorganization of cognitive
functions is a larger problem in language recovery research.
Similar fMRI results have been reported across different types
of language impairments, with patients showing both greater
perilesional and greater contralesional activation than controls (see [18, 25] for discussion). However, as with reading,
early stages of language recovery are more linked to contralesional activation, while later stages of recovery are associated
with perilesional activation [23]. Further challenging the idea
that contralesional activation reflects functional reorganization is the finding that transcranial magnetic stimulation to
contralesional areas in aphasic patients has surprisingly been
shown to improve language production [26]. This finding
has been interpreted to indicate that contralesional activation may reflect the engagement of a dysfunctional process
that inhibits the ability to do the task. However, when a
second stroke damages contralesional regions, whatever language has recovered is severely impacted, in both language
production (e.g., [27–29]) and reading [30], suggesting that
the contralesional region had been supporting the residual
language capacity following the left hemisphere stroke.
With all of these difficulties in interpreting changes in
activation, it is possible that traditional, univariate activationbased approaches to fMRI are not well suited to address issues
of the reorganization of function following stroke. An additional concern is that it is not clear that the functional takeover necessarily predicts changes in activation between the
patient and control populations. For example, both patient
and controls may rely on the same brain regions during
reading, but the function of the region is different between
the two populations. Alternative methods for analyzing fMRI
may be better at distinguishing the functional take-over and
compensatory masquerade hypotheses.
The functional take-over and compensatory masquerade
hypotheses clearly make testable predictions, given the appropriate analysis methods. According to the compensatory

Neural Plasticity
masquerade, contralesional and perilesional regions in the
patient’s brain should be doing the same cognitive function
as the corresponding regions in the control group. According
to the functional take-over hypothesis, the function of those
regions in the patient’s brain should be different than the corresponding regions in the control group and more similar to
the normal functions of the damaged region. To address this
prediction, it is necessary to identify fMRI methods that can
decode what function is being processed by a region, rather
than just finding differences in activation level. Here, we use
a multivariate approach to analyzing fMRI data, specifically
representational similarity analysis (RSA, [31]).
Using RSA, we compare word-similarity measures
derived from computation models of different readingrelated functions to the patterns of activity distributed across
a brain region for individual words. Following existing
hypotheses about the neural plasticity of reading following
damage, we focus on two levels of representation: lowlevel visual representations of the stimulus code all visual
inputs into basic visual features such as oriented edges and
orthographic representations segment words into a sequence
of ordered letter identities and abstract away from low-level
visual information like case, font, or location. Other levels
of representation like phonological representations, which
encode the sequence of sounds that correspond to the word
being verbalized, and semantic representations, which denote
the meaning of the word, are also involved in word reading
but are outside of the scope of the current investigation.
These levels can be distinguished based on which stimuli
are represented similarly. For example, the letters d and b
are similar to each other at a visual level of representation,
while d and D share fewer visual features. In contrast, at
an orthographic level, d and D are examples of the same
orthographic letter identity, while d and b are not and thus
map onto different representations [32].
Several recent studies have used RSA logic to investigate
levels of representation in reading in the unimpaired brain.
Rothlein and Rapp [32] focused their investigation on the
representation of single letters. They found that the left
VWFA was selectively tuned to orthographic representations
of letters and a right lateralized occipital region is tuned to
low-level visual representation of letters. Fischer-Baum and
colleagues [24] focused on whole word representations. They
found that the VWFA processes orthographic information
about words, while the right homologue processes visual
information about the same stimuli. In addition, they found
that distributed patterns of activity in the left angular gyrus
implicates the region in orthographic processing, while the
pattern of activity in the right angular gyrus did not correspond to any of the tested reading-related functions. These
studies demonstrate how RSA can be used to decode readingrelated functions from distributed patterns of activation.
In the current study, we will apply this approach to
the question of the neural correlates of reading recovery.
Specifically, we report an fMRI study that compares a single
case of an individual, CH, with a chronic reading and writing
impairment following a hemorrhagic stroke with a control
group. Using RSA, we will determine the informational
content of perilesional and contralesional regions in both

3
the patient and the controls by comparing the similarity
structure of the region’s BOLD activation to individual
words to a similarity structure predicted by computational
models of representations during reading. In this way, we can
adjudicate between the functional take-over hypothesis and
the compensatory masquerade hypothesis.

2. Case Study
CH was a 52-year-old right-handed male with a left hemisphere lesion resulting from a hemorrhagic stroke in 2008.
He started coming to the lab in December 2011, 37 months
following his stroke. Data for the current project was collected
between 2013 and 2015. Functional neuroimaging data was
collected in July 2014. He has a Master’s degree in Chemical
Engineering and owned his own company prior to his stroke.
As we show below, at the chronic stage, CH was able to
read some familiar words despite serious difficulties in
processing the identities of individual letters. Similar patterns
have been reported previously in the literature [33] and
have led researchers to posit an alternative reading pathway
available to all readers [34]. Alternatively, during the course of
recovery, CH’s brain may have changed to allow for a different
type of orthographic processing not available to other readers.
These alternative accounts of CH’s impairment are examples
of the compensatory masquerade and the functional take-over
hypotheses, respectively.
2.1. Lesion Localization. To localize CH’s lesion, a T1weighted structural image (TR/TE = 8.4/3.9 ms; FA = 8
degrees; matrix size = 256 × 256; FOV = 240 mm; slice thickness = 1.0 mm thick sagittal slices) was obtained. His lesion
was segmented manually (following Schnur et al. [35]) and
the structural scans, including the lesion mask, were warped
and registered to an intermediate template using a symmetric diffeomorphic registration algorithm ([36]; see also
http://www.picsl.upenn.edu/ANTS/). Figure 1 shows nine
axial slices from CH’s T1 scan. From the intermediate template, scans were then mapped to the “N27 Colin” normalized
template in MNI space [37] and then resampled to 1 mm axial
resolution using AFNI’s 3dresample. Lastly, percent damage
was calculated in Automated Anatomical Labeling (AAL)
space [38] using VoxBo (http://www.voxbo.org).
CH’s lesion included a large portion of the left hemisphere
(38% of all left hemisphere voxels). Damage was most
extensive in the parietal lobe, including nearly all of the left
angular gyrus (90% damaged) and a large portion of the
supramarginal gyrus (70% damaged), as well as portions of
the left superior (49% damaged) and inferior parietal lobules
(67% damaged). There were also extensive temporal damage,
in the superior (51% damaged), middle (54% damaged), and
inferior (20% damaged) gyri, the temporal pole (26% damaged), and occipital damage, in the superior (35% damaged)
and middle (68% damaged) occipital gyri and the fusiform
gyrus (18% damaged). Finally, the lesion extended anteriorly
to the precentral gyrus (19% damaged) and the posterior
portion of the left inferior frontal gyrus, specifically the pars
opercularis (30% damaged).

4

Neural Plasticity

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Figure 1: Visualization of CH’s lesion from nine axial slices of his T1 scan with 10 mm between each slice going from inferior (a) to superior
(i) location.

2.2. Behavioral Testing. CH received a standard battery of
language and cognitive control tasks. His speech perception
was assessed as part of a case-series study [39]. CH fell
within age- and education-matched controls on both word
and nonword minimal pair discrimination tasks, auditory
lexical decision, and picture-word matching with auditory
foils (p’s > .20). In single word, picture matching task [40], CH
was always able to correctly indicate when the picture and the
word matched and also made no errors when the word and
the picture were unrelated phonologically or semantically
and only one error with phonological foils. However, he made
many false alarms with semantically related foils (29/54, 54%
correct), suggesting that he has a semantic impairment or
an impairment in accessing semantics from the auditory
modality.
There is some evidence that the semantic impairment
in speech perception is multimodal. Speech production was
also impaired. CH was given the Philadelphia Naming Test

[41], on which he was only able to name 87/175 pictures.
The majority of his errors (80%) are semantic in nature,
evenly split between semantic errors and picture descriptions.
He also performed below the control range (47/52) on the
three pictures’ version of the Pyramids and Palm Trees Test
[42]. Therefore, CH appears to have an amodal semantic
impairment.
CH also has a striking written language impairment, even
on tasks that require processing single letters. CH’s visual
processing of letters appears to be largely intact. Low-level
visual processing was evaluated by a task in which he had to
directly copy visually presented letters in the same case. He
was nearly perfect at this task (155/156; 99%). He was also
able to judge whether a written symbol was a real letter
or a pseudoletter (88/88; 100%). Following Schubert and
McCloskey [43], this pattern suggests that CH has intact
processing up to a visually processing level that stores shapes
that correspond to familiar letters. However, on tasks that

Neural Plasticity
require orthographic processing of single letters, that is,
processing letter identity information that abstracts away
from case, font, or modality, CH is quite impaired. He
has difficulties naming visually presented letters (26/104;
25%) and copying visually presented letters in the opposite
case (57/84, 68%). Therefore, we conclude that CH either
has difficulties representing abstract letter identities at an
orthographic level of representation that abstracts away from
information about case or difficulties accessing these abstract
letter identity representations at the orthographic level from
the preceding level of representation involved in recognizing
familiar letters.
CH also had difficulties processing abstract letter identities from other modalities of presentation. For processing
letter identities from the tactile modality, foam letter magnets
(approximately 1.75 inches tall) were handed to CH while his
eyes were closed. CH had difficulty naming these tactilely
presented letters (8/52; 15%) and had trouble copying these
tactilely presented letters in the opposite case (10/22; 45%),
suggesting that his impairment at the level of abstract letter
identities was independent of the modality of input. CH
also had difficulties processing letter identities from the
auditory modality, for example, with difficulties in writing
letters to dictation (13/52; 25%). Given that CH has difficulties
processing abstract letter identity information from a variety
of modalities, we assume that he has a general impairment
in representing abstract letter identity information at an
orthographic level of representation.
To assess his ability to read and write whole words, CH
was given the same set of 80 high-frequency words between
3 and 7 letters long and 20 pronounceable pseudowords
in four tasks: reading aloud, written spelling, oral spelling,
and recognition of orally spelled words. This final task has
been argued to tap into central reading processes while
bypassing the visual system [44]. CH was unable to read
or write any of the pseudowords (0/20 in all four tasks),
suggesting that reading and writing pseudowords rely on
processing individual letter identities at the orthographic
level of representation impaired in this participant. He also
was unable to orally spell any of the words or recognize
a single orally spelled word (0/80 on both tasks) and was
only able to correctly write 2 of the 80 word targets (2/80;
2.5% correct). CH’s difficulty with processing abstract letter
identities had a profound impact on his ability to write words
and to recognize orally spelled words, suggesting that these
tasks all rely on intact processing at an orthographic level of
representation.
CH was also impaired at reading words aloud but was
markedly better on this task than the other three, correctly
naming 48/80 (60%) of the written words. To further assess
CH’s ability to read words, he was given the Johns Hopkins
University Dyslexia Battery [45]. Overall, he correctly read
89/328 of the stimuli (27%). He was more accurate with words
(30%) than nonwords (3%, 𝜒2 = 30.2, 𝑝 < .0001) and more
accurate with high-frequency words (40%) than low frequency words (20%; 𝜒2 = 7.9, 𝑝 < .01). He showed no effects
of spelling-to-sound regularity, reading regular-consistent
words (33%), regular-inconsistent (33%), and exceptional

5
words (30%) with the same level of accuracy (𝜒2 = 0.1,
𝑝 > .95). This pattern suggests that whatever residual reading
ability CH has, it is sensitive to lexical knowledge about the
frequency with which words have been encountered, but
not by knowledge of spelling-to-sound mappings, which
would predict some nonword reading capacity and effects of
spelling-to-sound regularity.
Note that this is likely an underestimate of his visual
word processing ability. CH’s difficulties in picture naming
described above suggest language production problems mapping from a semantic system to the phonological system.
This language production problem should contribute to CH’s
difficulties in reading words aloud, particularly since he is
unable to read a single nonword [46]. An alternative test of his
visual word processing ability is lexical decision, tapping into
whether he can recognize that letter strings comprise familiar
words. CH was given PALPA 25 which includes 60 words split
evenly between high and low frequency and high and low
imageability and 60 pseudowords. He was correct on 100/120
(83%) trials, below controls who are nearly perfect on this
task, but well above chance. Nearly all of his errors with word
stimuli (8/9) were made with words that were of both low
imageability and low frequency, indicating, again, that his
residual reading ability is limited in its scope to words that
are high in frequency and/or imageability.
Further tests were carried out to test whether CH’s
preserved word processing could be attributed to certain
frequent words being recognized perceptually as familiar
objects. In one task, we had CH read the same set of 80
familiar words that were presented in UPPER case, lower
case, and aLtErNaTiNg case, counterbalanced across a series
of sessions. In another task, CH had to make lexical decisions
to the same set of 120 words and 120 pseudowords under the
same three conditions, counterbalanced across a series of
sessions. The logic behind this case manipulation was that CH
may have previously seen these words in upper or lower case
but would have no previous perceptual experience with these
words in alternating case (see [33, 47] for similar logic). If
CH’s ability to recognize these words depends on familiarity
with the perceptual properties of the word, then his performance should be better with upper and lower case words
than with alternating case words. Alternatively, if his ability
to recognize these words depends on more abstract properties
about letter identity and order, then his performance should
not be influenced by the case manipulation. In both the reading aloud and lexical decision task, there was no difference in
performance for the upper and lower case words than for the
alternating case words (reading aloud: combined UPPER and
lower case: 56/160 (35%), aLtErNaTiNg case: 27/80 (34%),
𝜒2 = 0, 𝑝 = 1.0; lexical decision: combined UPPER and
lower case: 361/480 (75%), aLtErNaTiNg case: 183/240 (76%),
𝜒2 = .05, 𝑝 > .82).
Despite severe difficulties in orthographic processing for
single letters, CH is able to process some written words. This
pattern of performance is not predicted by most theories of
visual word processing. Theories typically assume that central
reading processes—activating long-term memories of the
orthographic representations that correspond to familiar

6
words, semantic representations of the meaning of those
words, and phonological representations of pronunciations
for both familiar and unfamiliar words—are all necessarily
mediated by a level that represents abstract letter identities [43, 48–50]. Furthermore, this pattern is inconsistent
with the patterns that are frequently observed in acquired
alexia. Most patients with severe problems processing letter
identities from visual input also have difficulties in reading
words (e.g., [21, 43, 51]), and intact letter identification
with impaired whole word reading has been argued to
be the basis of the letter-by-letter reading strategy frequently observed in individuals with alexia (e.g., [10, 16]).
However, CH’s pattern of performance is also not completely unprecedented. Howard [33] reported a similar case
of an individual whose abstract letter processing ability
was essentially at chance but who was able to read some
words (30–40%). While it may be rare, acquired reading
deficits in which abstract letter identity processing is more
extensively impaired relative to word reading appear to be
possible.
Brunsdon and colleagues [34] use this pattern to argue
for an alternative route to reading that does not depend
on the same level of abstract letter identity representation
responsible for processing case-free letter identities.1 Instead,
this theory posits a level of representation that identifies
individual shapes as letters in a manner that abstracts away
from font but not from case, which precedes the case-free
abstract letter identity representations. Furthermore, this
theory assumes that there can be direct mappings from these
font- but not case-free representations onto stored long-term
memory representations of the spellings of familiar words.
Assuming damage to the level of case-free representations
and this alternative route to recognizing familiar words can
explain why CH cannot match letters across case but can read
UPPER case, lower case, and aLtErNaTiNg case words equally
well.
According to Brunsdon and colleagues [34], this route is
available for all readers. Therefore, this argument is a version
of the compensatory masquerade hypothesis, suggesting that
analysis of patient performance reveals a reading pathway
that exists even in the undamaged brain. Another possibility
is that, over the course of recovery, CH has developed this
alternative route, which is not part of the unimpaired reading
system. This argument is a version of the functional takeover hypothesis, with the damaged brain reorganizing to allow
for orthographic processing of familiar words in regions not
typically associated with an orthographic function.
Whether CH’s residual reading ability reflects functional
take-over of the reading system or reveals a compensatory
masquerade remains an open question. To address this issue,
CH and group of controls underwent fMRI scanning while
reading words. Using representational similarity analysis,
we will evaluate whether, contralesionally or perilesionally, CH shows evidence of a change in reading function,
specifically a shift in the neural locus orthographic processing, as would be predicted by the functional take-over
hypothesis.

Neural Plasticity

3. fMRI Study
3.1. fMRI Methods
3.1.1. Subjects. CH and 20 healthy, right-handed, Englishspeaking, adult volunteers (aged 18–30) with normal or
corrected-to-normal vision participated in the study (controls are the same as those previously reported in [24]). All
participants gave informed written consent. The study was
approved by the Rice University Institutional Review Board.
Participants were compensated $25 for their participation.
The entire experiment took approximately 1.5 hours.
3.1.2. Data Collection. Subjects were fitted with a 12-channel
head coil in a Siemens 3-T scanner at the Core for Advanced
MRI (CAMRI) at Baylor College of Medicine. First, a T1
anatomical scan with 192 1 mm axial slices was collected from
each participant. Then, participants underwent twelve function runs to measure BOLD activity during the experimental
task. BOLD activity was measured using gradient-echo T2∗ weighted echo-planar imaging (EPI) of the whole brain. 34
slices were acquired axially, for a voxel size of 3.4375×3.4375×
4 mm (TR 2 s, TE 30 ms, and flip angle 90∘ ).
3.1.3. Task. An event-related design was used. Each of a single
list of 40 words (5 proper names, 35 critical words for the
analysis) was presented in random order exactly once during
each of 12 functional runs. Participants were instructed to
press a button whenever a proper name was presented. On a
given trial, a fixation cross was presented for 500 ms, followed
by a word for 500 m with a trial onset asynchrony of 4 s.
Words were presented in all capital letters in Arial font (size
36). On approximately 25% of the trials, a blank screen was
presented instead of a word, in order to obtain an estimation
of baseline activity [31]. Visual stimuli were presented on a
projection screen with an LCD projector and viewed through
a mirror attached to the head coil. The 35 critical words
were chosen to be intuitively and computationally distinguishable between the four levels of representation tested in
the previous paper—visual, orthographic, phonological, and
semantic. To adjudicate between the functional take-over and
compensatory masquerade hypothesis, we focus on only the
visual and orthographic levels of representation.
3.2. Theoretical Similarity Matrices. For the set of 35 critical
words, similarity matrices were calculated based on computationally explicit theories of representation at different levels
in the reading system. For the visual level of representation,
we used a binary silhouette of each word and computed
the pixel-wise overlap of the two images [31]. Orthographic
similarity was calculated on the basis of an open-bigram
model [52], in which words are represented by multiletter
units that reflect ordered pairs of letter identities that are not
necessarily adjacent to each other in the word. This type of
open-bigram model closely matches the nature of the orthographic code that has been previously proposed to reflect the
representational content of the left VWFA [53]. We used the
MatchCalculator tool, developed by Colin Davis, to calculate

Neural Plasticity

7

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Pie
Sew
Shoe
Sight
Sleigh
Suit
Taxi
Thigh
Think
Tie
White
Yacht

(b)

Figure 2: Lower off-diagonal for the two theoretical similarity matrices based on (a) visual and (b) orthographic levels of representation.

the similarity between words according to this theory (http://
www.pc.rhul.ac.uk/staff/c.davis/Utilities/MatchCalc/)2 . Figure 2 depicts a visual representation of the off-diagonal of
the similarity matrices generated by these different theories of
cognitive representation, in which an entry of 𝑖, 𝑗 in a given
matrix indicates how similar word 𝑖 is to word 𝑗 using a given
metric. The Spearman correlation between the two similarity
matrices was .12.
3.3. Data Analysis
3.3.1. fMRI Data Preprocessing. Data preprocessing was done
using SPM12 (University College London, 2012) on T2∗ weighted functional images. Preprocessing included motion
correction, coregistration with the T1, and slice time correction. For the RSA, we chose to forgo spatial smoothing,
since differences across adjacent voxels may contain valuable
information in the RSA [31]. The segmentation step was
carried out, producing forward and backward deformation
fields to map to and from MNI space as well as a grey matter
mask.
3.3.2. Univariate Analysis. To determine whether the experiment elicited a typical reading network in a standard
fMRI analysis, we contrasted all words with fixation, using
SPM12. Additional preprocessing steps were applied. Spatial
smoothing was done using 8 mm full width at half maximum
Gaussian smoothing kernel and the images were warped into
MNI space at a resolution of 2 × 2 × 2 mm with 4th-degree Bspline interpolation. For each participant, a 1st-level analysis
used a contrast to compare all words, though not proper
names, to a fixation baseline. Six motion parameters and

scanner drift were included as covariates in the univariate
analysis. A 2nd-level analysis used a Crawford’s modified 𝑡test [54] to determine if the t-maps that resulted from the
1st-level analysis for CH fell outside of the distribution of the
control group.
3.3.3. Representational Similarity Analysis. For the RSA, no
smoothing or normalization was applied during preprocessing. Beta-weights for each word in each run against fixation
were obtained by a general linear model predicting BOLD
response, which included the timing of each individual word
(modeled as an event) deconvolved with a hemodynamic
response function and six motion parameters and scanner
drift as nuisance variables. By averaging the beta-weights
across 12 trials per subject, we obtained 35 beta-weight maps
for each subject, with each map reflecting the brain’s response
to each word in the experiment. These beta-weight maps
were then mean centered within each subject. ROI analyses
were applied to these 35 individual-word beta-weight maps
for each participant. For each ROI, a vector of beta-weights
for the voxels within that ROI was extracted for each of the
35 words. A similarity matrix of word-to-word similarity for
this region was calculated based on a Spearman correlation
of the beta-weight vectors for each word to every other
word.3 Each entry in the resulting similarity (correlation)
matrix therefore represented how similar the distributed
pattern of activity within the ROI is between two stimuli. The
resulting similarity matrix of pairwise correlations was then
compared with visual and orthographic similarity matrices
described above, using a Spearman correlation of the offdiagonal values. We refer to the Spearman rho value for the
correlation between the brain-based similarity matrix and

8
the visual similarity matrix as the visual similarity index and
the Spearman rho value for the correlation between the
brain-based similarity matrix and the orthographic similarity
matrix as the orthographic similarity index. For each participant (both controls and CH) and for each ROI, two values
were calculated—visual and orthographic similarity indices
for the region.
3.3.4. Anatomical ROIs. A challenge to the RSA approach is
selecting the appropriate regions of interest for the analysis.
One option is data driven, selecting ROIs on the basis of
the whole brain analysis, identifying regions in which CH
shows more activation than controls and investigating the
function of these regions in CH and controls. A second option
is hypothesis driven, selecting regions anatomically, based
on the hypotheses that reorganization is happening either in
tissue just adjacent to the lesion (perilesionally) or in the right
hemisphere homologues of damaged regions that are known
to be relevant to reading in the undamaged brain. For the
current study, we opted for the hypothesis driven approach
to selecting ROIs. Because we are investigating only a single
case study, the results of the whole brain analysis may be
unreliable, with both type 1 and type 2 errors, making it a
poor source for selecting ROIs. Additionally, as discussed
above, it is not clear that the type of neural plasticity that
we are investigating with RSA will result in changes in
activation, especially if the function of a region switches from
one reading-related function to another. Furthermore, the
goal of the current study is to investigate specific claims
about perilesional and contralesional reorganization, and
there is no guarantee that we will find areas of increased
activation in the whole brain analysis for a single subject
in either of those critical regions. Therefore, we limited our
ROIs to anatomical regions defined on the basis of specific
hypotheses about the reorganization of function. Specifically,
we looked at three types of region of interest—CH’s lesion
location in unimpaired participants, contralesional regions of
interest, and perilesional regions of interest. We discuss the
motivation for each type of ROI below.
One of the goals of the current study is to determine
if the function that is normally computed by the region of
cortex that is damaged in CH’s brain has moved to a
different region. Therefore, one ROI involves looking at the
information processing capacity of the CH’s damaged cortex
warped onto the control participants. A lesion mask for CH
was traced using MRIcron with “1” assigned to damaged
voxels and “0” to other voxels and normalized to the MNI
template brain using SPM12 and then warped into each
subject’s native space.
A second set of ROIs focused on decoding activation in
contralesional regions. Specifically, we focus on regions that
are (1) damaged in CH and (2) have been argued to have leftlateralized processing of orthographic information in control
participants. We will then evaluate whether the right hemisphere homologue of these regions compute different reading
functions in CH compared to controls. Fischer-Baum et al.
[24] identified orthographic processing in the left VWFA and
left angular gyrus (ANG), two regions partially damaged in

Neural Plasticity
CH. Therefore, left and right vOT and left and right ANG
ROIs were used to analyze whether there is contralesional
functional take-over in CH. The left and right VWFA ROIs
were 12 mm3 radius spheres centered on the MNI coordinates [±45, −57, −12] (based on Talairach coordinates for the
VWFA form [55], MNI coordinates based on [56]) and were
created using MRIcron [57] and left and right ANG were
taken from the Automated Anatomic Labeling (AAL) Atlas
[38].
A third set of ROIs focus on functional take-over in cortical regions adjacent to the lesion. Given CH’s large lesion, we
subdivided his perilesional space into five regions of interests. These masks were created by identifying undamaged
voxels from different Automated Anatomical Labeling (AAL)
regions. The fusiform mask was all of the voxels outside of
CH’s lesion mask in the left fusiform gyrus. The inferior
temporal mask was all of the voxels outside of CH’s lesion
mask in the left middle and inferior temporal gyri. The medial
temporal mask was all of the voxels outside of CH’s lesion
mask in the left hippocampus, parahippocampal gyrus, and
amygdala. The anterior temporal mask was all of the voxels
outside of CH’s lesion mask in the left superior and middle
temporal pole. Finally, the parietal mask was all of the voxels
outside of CH’s lesion mask in the left supramarginal gyrus,
angular gyrus, superior parietal lobule, and inferior parietal
lobule.
All masks were warped into each subject’s native space
at the resolution of the T2∗ image using normalize function
from SPM12 and the backward deformation fields produced
by the segment function in SPM12 during the preprocessing
stage, with nearest neighbor interpolation. Second-level analyses were carried out across subjects for both similarity index
values in each ROI. For each ROI, one set of tests focuses on
determining the extent to which each ROI computes visual
and orthographic information in the controls. A second set
of analyses investigates whether the function of the region has
shifted to orthographic processing in the CH, relative to controls. Our dependent measure is the difference between the
orthographic and visual similarity indices in contralesional
and perilesional ROIs. For this analysis, statistical analyses
were carried out using a one-tailed Crawford’s modified 𝑡-test
[54] under the null hypothesis that CH’s difference between
the orthographic and visual similarity indices for a given
region was not greater than the distribution of the control
participants.

4. Results
4.1. Univariate Analysis. Figure 3 shows the results of the
univariate analysis warped to CH’s anatomical scan, with an
uncorrected 𝑝 < .05 comparing CH’s words versus fixation
analysis to the distribution of the controls words versus fixation analyses, using a Crawford’s modified 𝑡-test. Bilaterally,
CH shows more activation than controls in a number of
regions: occipital cortex (calcarine sulcus and lingual gyrus),
the insular and cingulate cortex, and the inferior frontal
gyrus. In addition to these bilateral regions of activation,
CH showed perilesional activation in the middle occipital

Neural Plasticity

9

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(j)

(k)

(l)

(i)

CH activation versus controls: Crawford’s modified t-test: uncorrected p < .05

Figure 3: Results of the whole brain univariate analysis identifying regions where CH shows greater activation in words versus fixation than
the control distribution plotted in CH’s native space (uncorrected p < .05).

gyrus, the posterior middle temporal gyrus and the postcentral gyrus. He also showed contralesional activation to
damaged left hemisphere regions typically associated with
word reading, including the right midfusiform gyrus and the
superior temporal gyrus. Finally, he showed greater activation
to words than controls in other right hemisphere regions like
the right parahippocampal gyrus and the superior frontal
gyrus.
4.1.1. CH Lesion ROI. Figure 4 plots the average of the 20
control participants for each of the visual and orthographic
similarity indices for the ROI based on CH’s traced lesion. A
one-sample 𝑡-test reveals that, in the CH Lesion ROI, both the
visual (.013; 𝑡(19) = 2.23, 𝑝 < .05) and orthographic
(.030; 𝑡(19) = 4.03, 𝑝 < .001) similarity indices are
significantly different than zero. A paired 𝑡-test indicates
that the lesioned region is marginally more involved in
orthographic processing than visual processing (𝑡(19) = 2.01,
𝑝 = .059).
4.1.2. Contralesional ROIs. Control results for the ROIs used
in this analysis are reported in Fischer-Baum and colleagues
[24]. Table 1 reports the range of control orthographic and

visual similarity index values for these contralesional regions,
as well as the subsequent perilesional ROI analyses, along
with CH’s value for the orthographic and visual similarity
index in each region and CH’s rank among the control
participants. Figure 5 shows the results of the analysis for
the portion of the left and right ventral occipitotemporal
cortex frequently referred to as the visual word form area and
its right hemisphere homologue. For controls, a significant
interaction between hemisphere and orthographic versus
visual representation was observed in this region (𝐹(1, 19) =
11.3, 𝑝 < .01). Consistent with Dehaene and Cohen [4], in the
left VWFA, the orthographic index (.024) was higher than the
visual index (.006), while in the right VWFA the visual index
(.021) was higher than the orthographic index (.003).
CH’s lesion extends into this region in the left hemisphere.
Under the contralesional version of the functional take-over
hypothesis, we might predict that his right VWFA has reorganized, changing its function from visual to orthographic processing [16]. Figure 5(c) presents a box-and-whiskers plot for
the distribution of the difference between the orthographic
and the visual indices for all of the control participants in both
the left and right VWFA ROIs. While, on average, the orthographic index is higher in the left VWFA and the visual index

10

Neural Plasticity

Table 1: Range of the visual and orthographic similarity indices values along with CH’s value and rank among the 21 participants (20 controls
plus CH) for all 7 ROIs reported in the text.

Contralesional VWFA
Contralesional angular gyrus
Perilesional fusiform
Perilesional inferior temporal
Perilesional medial temporal
Perilesional anterior temporal
Perilesional parietal

Control range
−.029–.100
−.034–.039
−.040–.068
−.042–.067
−.038–.094
−.040–.057
−.047–.057

Visual
CH index
−.032
−.037
−.003
−.009
−.025
−.004
.014

0.04

0.03

Similarity index

0.02

0.01

0
−0.01
−0.02

CH lesion
Visual
Orthographic

Figure 4: RSA results for the CH Lesion ROI. The graph reports the
average (Spearman) correlation between each subject’s brain-based
similarity matrix with the two theoretical similarity matrices based
on computational models of visual and orthographic representation.
Error bars represent ±1 SEM.

is higher in the right VWFA, it is not true for all participants. The black dot depicts the difference between CH’s
orthographic and visual indices in his intact right hemisphere. Unlike the majority of control participants, CH
shows more orthographic than visual processing in the right
hemisphere homologue of the VWFA. CH ranks below all 20
control participants in terms of the visual similarity index
and above all but 5 controls in the orthographic similarity index. Using a one-tailed, Crawford’s modified t-test,
we found that CH showed greater orthographic-relativeto-visual processing in the right hemisphere than controls
(𝑡(19) = 2.09, 𝑝 < .05).
Figure 6 shows the results of the same analysis for the
other contralesional ROIs, the left and right angular gyrus.
For controls, neither the left nor the right angular gyrus
shows any evidence of visual processing (p’s > .27). However,
there is evidence for left-lateralized orthographic processing,

CH rank
21
21
17
17
19
11
9

Control range
−.050–.083
−.041–.053
−.049–.056
−.051–.085
−.031–.067
−.041–.060
−.076–.120

Orthographic
CH index
.032
.057
.033
.022
.026
.013
.080

CH rank
6
1
8
9
8
11
3

with the left angular gyrus (.019) having a significantly larger
orthographic similarity index than the right angular gyrus
(−.002; 𝑡(19) = 2.45, 𝑝 < .05). As with the left vOT, CH’s
stroke has damaged a large portion of the left angular gyrus.
We analyzed whether CH uses the right angular gyrus to process orthographic information. Figure 6(c) depicts the distribution of the difference between the orthographic and the
visual indices for the 20 control participants in both the left
and right angular gyrus using a box-and-whiskers plot, with a
black dot indicating CH’s orthographic minus visual similarity index for his intact right hemisphere. CH ranks below all
20 control participants in terms of the visual similarity index
and above all 20 controls in the orthographic similarity index
for this ROI. Unlike the control participants, CH had a much
larger orthographic similarity index relative to his visual
similarity index in the right angular gyrus. This value was
significantly above the distribution of corresponding values
in the control population (𝑡(19) = 3.13, 𝑝 < .01).
4.1.3. Perilesional ROIs. Figure 7 plots the results of ROI
analysis for the five perilesional regions of interest, left
hemisphere fusiform, inferior temporal, medial temporal,
anterior temporal, and parietal regions adjacent to CH’s
lesion. Using a one-sample 𝑡-test, the visual similarity index
was significantly different than zero for control participants
in the fusiform (.013; 𝑡(19) = 2.10, 𝑝 < .05), inferior
temporal (.016; 𝑡(19) = 2.80, 𝑝 < .05), and medial temporal
(.017; 𝑡(19) = 2.33, 𝑝 < .05) perilesional ROIs, but not
for the anterior temporal or parietal ROIs (p’s > .35). The
orthographic similarity index was significantly different than
zero for control participants in the medial temporal lobe
(.016; 𝑡(19) = 2.45, 𝑝 < .05) and marginally different than
zero in the anterior temporal lobe (.014; 𝑡(19) = 1.97, 𝑝 =
.064), but not in the other ROIs (p’s > .11).
Figure 7(c) depicts the distribution of the difference
between the orthographic and the visual indices for the 20
control participants in all five perilesional ROIs, with the
black dot indicating CH’s difference value. There are marginally significant difference between CH’s difference value and
the distribution of the control difference values in the perilesional parietal ROI (𝑡(19) = 1.73, p = .050) and medial temporal ROI (𝑡(19) = 1.49, 𝑝 = .076), but not in any of the other
perilesional ROIs (p’s > .12). The ranking results (Table 1) were
similarly ambiguous; CH did not have either the lowest or

Neural Plasticity

11

0.03

0.12

0.02

0.08

0.01

0.04

Orth-visual

Similarity index

0.04

0
−0.01

0
−0.04

Visual
Left VWFA
Right VWFA

(a)
(b)

Orthographic
−0.08
−0.12

Left VWFA

Right VWFA

(c)

Figure 5: Results of the left and right VWFA ROI analyses. (a) shows the location of the left and the right VWFA regions of interest. (b) plots
the average (Spearman) correlation between each control subject’s brain-based similarity matrix (separately for the left and right VWFA ROIs)
with the visual and orthographic similarity matrices. Error bars represent ±1 SEM. (c) depicts a box-and-whiskers plot for the distribution of
the difference between the orthographic and the visual indices for all of the control participants in both ROIs, with the black dot in the right
VWFA depicting CH’s difference score.

the highest visual or orthographic similarity index in any of
the regions. For the visual similarity index, he fell below the
median in the fusiform (17/21), inferior temporal (17/21), and
medial temporal (19/21) ROIs, at the median (11/21) for the
anterior temporal ROI, and slightly above the median (9/21)
for the parietal ROI. For the orthographic similarity index, he
fell above the median for all but the anterior temporal ROI,
in which his orthographic similarity index was the median
value (11/21). His orthographic similarity index in the parietal
perilesional ROI was higher than all but two of the control
participants.

5. Discussion
Following a hemorrhagic stroke, CH was left with a severe
written language impairment. Over several years, his familiar
word reading improved, though he remained completely
agraphic and had residual difficulties in processing abstract
letter identity information for single letters. The goal of the
current research is to investigate whether his residual reading ability was supported by functional take-over, whereby
damaged functions have been reorganized into different brain
regions, or whether it reflects a compensatory masquerade,
whereby recovered reading depends on an alternative neural
pathway that exists for all readers. By using an fMRI multivariate pattern decoding technique, we demonstrate that CH’s
brain shows evidence of functional take-over. Most clearly, we
observe contralesional reorganization. We looked specifically
at two right hemisphere regions that are homologous to the
left hemisphere regions that are damaged in CH and have
been shown to be important for orthographic information
processing in controls. The results of our analyses suggest that
CH is now using these right hemisphere regions to process

orthographic information in manner that is distinct from the
control participants.
As in previous research, we addressed this issue using
functional MRI, scanning CH while he read words and
comparing his results to a control sample (e.g., [13, 15, 16,
19]). Previous research has used this approach to identify
cortical regions that show more of an increase in activation to
written words relative to baseline for patients than controls.
Taking this same analysis approach with CH, we found a
familiar pattern of results. CH showed more activation than
controls in a contralesional region close to the right hemisphere homologue of the VWFA, as well as more activation
than controls in regions surrounding the lesion in the left
hemisphere and additional bilateral frontal activation.
What differences in activation between patients and
controls mean in terms of functional reorganization remains
an open question [25]. While these activation instances are
frequently interpreted as a shift in the locus of a cognitive
function, they have also been argued to reflect engagement of
processes not typically used by controls [22] or even dysfunctional processing that inhibits the patient’s ability to
perform the task [26, 58]. We, therefore, used an alternative
to these traditional univariate activation-based approaches,
a multivariate technique that allowed us to decode function
from a distributed pattern of brain activity [31]. Specifically,
using this analysis, we compare evidence for orthographic
processing and visual processing in different cortical regions.
In unimpaired participants, there was clear evidence that
CH’s damaged cortex is involved in both visual and orthographic processing when reading words but, tentatively, is
more involved in orthographic processing. It is worth noting
that this analysis is carried out over a large region of interest
and we are not concluding that this entire region is involved
with both visual and orthographic processing of written

12

Neural Plasticity

(a)

0.04
0.12

0.08

0.02

0.04

0.01
Orth-visual

Similarity index

0.03

0
−0.01
−0.02

0

−0.04

Visual

Orthographic

Left ANG
Right ANG

−0.08
−0.12

(b)

Right ANG

Left ANG
(c)

Figure 6: Results of the left and right angular gyrus (ANG) ROI analyses. (a) shows the location of the left and the right ANG regions of
interest. (b) plots the average (Spearman) correlation between each control subject’s brain-based similarity matrix (separately for the left and
right ANG ROIs) with the visual and orthographic similarity matrices. Error bars represent ±1 SEM. (c) depicts a box-and-whiskers plot for
the distribution of the difference between the orthographic and the visual indices for all of the control participants in both ROIs, with the
black dot in the right ANG depicting CH’s difference score.

words. Instead, it is likely that different subregions of the
ROI are responsible for processing visual and orthographic
information about written words. For example, CH’s lesion
does include damage to the middle and superior occipital gyri
as well as the inferior temporal lobe and the angular gyrus. It
is possible that visual processing in the occipital subregions of
the large lesion ROI are responsible for the conclusion that the
lesion ROI is involved in visual processing of written words,
while the temporal lobe and angular gyrus subregions of the
lesion ROI are responsible for the conclusion that the lesion
ROI is involved in orthographic processing of written words.
Reorganization of the damaged orthographic function
is clearest in the contralesional ROIs. We looked at two
left hemisphere regions that are largely damaged in CH
and in which controls show more evidence of orthographic
than visual processing (left VWFA and left ANG). In the

right hemisphere homologues of those regions, controls show
either the opposite tendency (right VWFA) or no difference
between orthographic and visual processes (right ANG).
Unlike controls, CH shows a greater tendency to process
orthographic information than visual information in both
of these contralesional regions. CH uses these contralesional
regions to compute a different function than that same region
in controls. Furthermore, the function that CH is computing
in these regions is similar to the function that controls are
computing in tissue that CH no longer has following his
stroke. This pattern of results is precisely the pattern that
would be predicted if there is contralesional functional takeover, whereby the right hemisphere takes over the function
of the damaged left hemisphere. At least in right hemisphere
regions contralateral to the dyslexia-inducing lesion, there
is evidence for functional reorganization in CH, with the

13

(a)

(b)

0.08
0.04
0
−0.04
Parietal

Anterior temporal

Medial temporal

−0.12

Fusiform

−0.08
Inferior temporal

Visual (lighter color)
Orthographic (darker color)

Parietal

Anterior temporal

Medial temporal

Orth-visual

0.12

Inferior temporal

0.04
0.03
0.02
0.01
0
−0.01
−0.02

Fusiform

Similarity index

Neural Plasticity

(c)

Figure 7: Results of the perilesional ROI analyses. (a) shows the location of the five regions of interest: fusiform (green), inferior temporal
(purple), medial temporal (red), anterior temporal (blue), and parietal (yellow) regions. (b) plots the average (Spearman) correlation between
each control subject’s brain-based similarity matrix with the visual and orthographic similarity matrices. The white box indicates that for all
5 perilesional ROIs the lighter color is the visual similarity index and the grey box indicates that for all 5 perilesional ROIs the darker color
is the orthographic similarity index. Error bars represent ±1 SEM. (c) depicts a box-and-whiskers plot for the distribution of the difference
between the orthographic and the visual indices for all of the control participants in all five ROIs, with the black dot depicting CH’s difference
score.

region now processing orthographic information at least for
familiar written words. Recall that CH’s is still impaired at
tasks, even with single letters, that require crossing case or
modality. However, he remains relatively good at reading
familiar words even when they are presented in an unfamiliar
manner, like with alternating case. Therefore, we conclude
that the orthographic information being processed in his
right hemisphere supports the recognition of individual
letters abstracting away from certain visual properties of the
stimulus like font and location on the screen, but not other
visual properties, like case.
In terms of the ranking data, the results were the clearest
for the right ANG; CH has the highest orthographic similarity
index value in this region relative to the 20 controls and the
lowest visual similarity index value. However, one challenge
in interpreting this pattern is that it is unclear what type of
reading-related computation control participants are doing
in this region. Specifically, it is possible that controls have
an alternative reading pathway that goes through this region
but that does not respond obligatorily during the fMRI task
because controls read via a different pathway. If so, the
increase in the relationship between the pattern of activity
and the orthographic similarity metric in this region for CH
might reflect the use of a preexisting system that is silent
in the controls. The same argument cannot be made for the
right VWFA. In control participants, the pattern of activity in
the right VWFA correlates with the visual similarity metric,
indicating that this region is engaged during reading and is
doing visual processing. CH shows less visual processing and
more orthographic processing in this region than controls.
For this region, therefore, CH shows a clear shift in the
normal reading function which cannot be explained by
assuming a preexisting, but silent, reading pathway with
orthographic processing in the right VWFA.

There is more limited support for this type of neural
plasticity in perilesional regions. Regions that are superior/posterior to the lesion, in the perilesional parietal lobe,
and medial to the lesion, in the medial temporal lobe,
have a marginally greater tendency to process orthographic
information in CH than in the controls. We therefore do not
want to make any strong conclusions about whether or not
CH shows perilesional reorganization of function. The role
of contralesional and perilesional regions in recovery is an
open question in the neural plasticity of language [18, 25]. In
reference to this question, we can conclude that orthographic
functioning in at least one individual is reorganized from
the left to right hemisphere, with the possibility of additional
perilesional reorganization in this individual.
However, there remain some limitations of the current
study. First, the control group in this study is not ideal.
CH is compared to a group of young adults. It is possible
that the degree of orthographic and visual processing in the
right hemisphere may depend on age. In general, aging has
been associated with a reduction of hemispheric asymmetry
[59]. While this reduction in hemispheric asymmetry has not
been shown for reading specifically, it is possible that CH’s
right hemisphere responds differently to written words than
the control group because he is older than the controls, not
because he had a stroke. Age-matched control participants
could address this concern.
Second, while the similarity index effects reported here
are significantly different than zero, they are still exceedingly
small. Similarity indices can vary between −1 and 1, but, at
best, we observed values around .05 in the current study.
These low correlations can partially be explained by noise in
the data, but the values obtained in the current study are likely
well below the noise ceiling (Nili et al., 2014). Therefore, we
conclude that computational models used to compute the

14
similarity matrices are only approximations of the neural
computations in the regions that we are investigating. The
fact that, in controls, the left VWFA correlates higher with
the orthographic than the visual similarity matrix suggests
that the orthographic theory is closer to the actual neural
computations of that region than the visual theory. However,
the bigram model used here to compute orthographic similarity does not fully capture how words are processed in this
region, potentially because it is not the correct theory of
representation and processing at an orthographic level.
Third, this is a single case study of an individual who,
despite having recovered some ability to read words, continues to have visual word processing difficulties well into the
chronic state. We would not want to argue from this one case
that all patients with acquired dyslexia show contralesional
reorganization. For one thing, because this is an exploratory
single subject analysis, we did not apply multiple comparisons
correction for the ROIs tested. Therefore, it is possible that
our results with this one subject reflect a type 1 error. A
larger study, with more participants, in which we correct for
multiple comparisons, is necessary to draw stronger conclusions about reorganization of the reading system following
stroke. Furthermore, it is worth noting that CH has a greater
residual reading impairment than many of the other cases
whose reading recovery has been studied with fMRI (e.g.,
[12, 13, 15]). CH’s continued impairment many years following
the stroke may reflect limits to contralesional reorganization,
and patients who make a more complete recovery may benefit
from perilesional reorganization rather than contralesional
reorganization. There are many open questions about how
individual differences in neural plasticity following stroke
related to differences in recovery. A large scale case-series
investigation using the methods outlined above is necessary
to address these questions.
Finally, because the current study is a single case investigation, we choose to select anatomical ROIs based on
specific hypotheses about contralesional and perilesional
reorganization, rather than functional ROIs based on regions
that are more activated in CH than controls in the whole
brain univariate analysis. As can be seen in Figure 3, there
are a number of anterior regions in which CH shows greater
activation than controls: bilateral inferior frontal gyrus and
cingulate and insular cortex. One limitation of the current
approach is we cannot interpret what this activity means,
either in terms of reorganization of function or in terms
of engagement of other cognitive processes like cognitive
control or working memory. Future research, with a larger
population, should apply this same logic of RSA to ROIs
selected on the basis of regions in which patients show an
increase in activation relative to controls. This approach will
be able to interpret what functional changes underlie these
increases in anterior activation.
The major contribution of this study is demonstrating
how new decoding fMRI techniques can provide stronger
evidence for functional reorganization of the reading network following stroke than traditional, univariate activation
analyses. These new techniques for analyzing functional neuroimaging data for information, not activation, have proved
to be powerful new tools in cognitive neuroscience [60–63].

Neural Plasticity
Studies of language recovery would benefit from using these
techniques. These techniques will allow us to map regions
that shift their function following damage in a way that
univariate, activation-based fMRI simply cannot. This study
provides a proof of concept that representational similarity
analysis can provide useful insights into functional reorganization following brain damage even with an individual
subject. Going forward, we anticipate that representational
similarity analysis will play a key role in addressing many
of the open questions about the neural plasticity of language
recovery: individual differences in neural and behavioral patterns of recovery, the relationship between spontaneous and
treatment-induced recovery, and how patterns of recovery
differ as a function of the domain of language impairment.

Competing Interests
The authors declare that they have no competing interests.

Acknowledgments
Support for the project came from the Rice University Faculty
Initiative Fund and the T.L.L. Temple Foundation. The
authors would like to thank CH for his willing participation in
the study as well as Heather Dial and Tao Wei for their help in
tracing CH’s lesion, Sherman Belton for his help testing CH,
and A. Cris Hamilton for help designing and implementing
the neuroimaging study.

Endnotes
1. It is possible that CH’s residual word reading is a
version of word superiority effect observed in unimpaired readers, whereby briefly presented letters are
easier to identify when embedded in words than when
embedded in nonwords or shown in isolation [64, 65].
However, CH does not show a word superiority effect
on letter identification. His letter naming performance
is identical when letters are embedded in words (13/60;
22%) and nonwords (13/60; 22%) and comparable to
his isolated letter naming ability (26/104; 25%). Another
possibility is that this residual word reading reflects the
patient’s ability to guess which word is present given
degraded, but not fully impaired, letter identity level
information (e.g., [21]). This account would predict that
CH should be more accurate at reading words with fewer
neighbors, as it is easier to guess those words with limited
orthographic information. However, when controlling
for frequency, imageability, and length, CH’s reading
ability was not influenced by neighborhood size (many
(>5) neighbors = 13/60; 22% versus few (≤1) neighbors
= 12/60; 20%), providing evidence against this guessing
account.
2. Given the design of our experiment, we cannot distinguish the font-free and case-free levels of representation
proposed by Brunsdon et al. [34]. In our experiment,
all of the words were presented in the same case, so we
were unable to determine whether a given region treated

Neural Plasticity
the same abstract letter identity in upper and lower
case (e.g., g and G) identically. Given CH’s behavioral
pattern of impairment, with poor cross-case or crossmodality letter processing but relatively good reading
even for words with alternating case, we expect that this
orthographic similarity measure will track font-free but
not case-free orthographic representations.
3. This procedure is slightly different than that used in
other RSA papers. Specifically, other researchers have
created representational dissimilarity matrices between
two brain patterns by computing 1 − 𝑟, where 𝑟 is Pearson’s correlation between two brain patterns. The choice
of Spearman’s correlation was motivated by concerns
about nonlinearities in the relationship between betaweights.

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