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Learning to read alters cortico-subcortical cross-talk in
the visual system of illiterates
Michael A. Skeide,
1
* Uttam Kumar,
2
Ramesh K. Mishra,
3
Viveka N. Tripathi,
4,5
Anupam Guleria,
2
Jay P. Singh,
4
Frank Eisner,
6
Falk Huettig
7
Learning to read is known to result in a reorganization of the developing cerebral cortex. In this longitudinal
resting-state functional magnetic resonance imaging study in illiterate adults, we show that only 6 months of
literacy training can lead to neuroplastic changes in the mature brain. We observed that literacy-induced neu-
roplasticity is not confined to the cortex but increases the functional connectivity between the occipital lobe
and subcortical areas in the midbrain and the thalamus. Individual rates of connectivity increase were signifi-
cantly related to the individual decoding skill gains. These findings crucially complement current neuro-
biological concepts of normal and impaired literacy acquisition.
INTRODUCTION
Learning to read is a profound cultural experience requiring systematic
instruction and intensive practice over months or years (1). Yet, hemo-
dynamic brain activity induced by perceiving printed words changes after
only a few weeks of training letter-sound links (2). Enhanced functional
selectivity to print emerges in parts of the visual system, that is, the bilateral
occipital cortices (3), and in a multimodal symbol processing region lo-
cated in the left temporo-occipital fusiform cortex (2,4,5). These findings
have revealed the important insight that literacy-related learning triggers
cognitive adaptation mechanisms manifesting themselves in increased
blood oxygen level–dependent (BOLD) responses during print processing
tasks (6,7). However, it remains elusive whether reading acquisition
also leads to an intrinsic functional reorganization of neural circuits.
Here, we used resting-state functional magnetic resonance imaging
(fMRI) as a measure of spontaneous neuronal activity to capture the
impact of reading acquisition on the functional connectome (8). In
a controlled longitudinal intervention study, we taught 21 illiterate
Hindi-speaking adults how to read Devanagari script for 6 months. The
goal was to compare the changes in resting-state fMRI data before and
after learning of the sample taught to read with those of a sample of nine
Hindi-speaking illiterates who did not undergo such instruction. Partici-
pants were recruited from the same societal community in two villages of a
rural area near the city of Lucknow in North India and matched for the
most relevant cognitive, demographic, and socioeconomic variables.
Given that becoming literate goes along with widely distributed
modulations of cortical responses to print, we assumed that the effects
of our intervention could be best captured with a two-step procedure.
First, we performed an unbiased network centrality analysis to explore
functional connectivity between each voxel and all other voxels of the
brain without predefining seed regions. The cluster of the most strongly
connected voxels was then used as a post hoc seed region to identify the
specific network driving the global change in functional connectivity.
RESULTS
Behavioral effects of practicing Devanagari script on letter
knowledge and word-reading skills
The behavioral effectiveness of the literacy instruction was reflected in
significant group (reading-trained individuals versus untrained illiter-
ates) by time (before versus after intervention) interactions of letter
knowledge [F
1,28
= 17.80, P< 0.001, h
2
=0.39;2×2mixedanalysis
of variance (ANOVA)] and word reading (F
1,28
=15.96,P< 0.001, h
2
=
0.36; 2 × 2 mixed ANOVA). Both interactions were driven by signif-
icant improvements of the trained group (letter knowledge: z=4.20,
P< 0.001, r=0.65;wordreading:z= 3.83, P< 0.001, r= 0.59; Wilcoxon
signed-rank tests) that were not observed in the untrained group (letter
knowledge: z=0.41,P=0.684;wordreading:z=0.37,P=0.715;Wilcoxon
signed-rank tests) (Table 1).
Resting-state network centrality changes in the bilateral
pulvinar nuclei and the right superior colliculus
Initially, we investigated in a voxel-wise fashion at the whole-brain level
whether the experience of becoming literate modifies network nodes of
spontaneous hemodynamic activity. Therefore, we comparedtraining-
relateddifferences in the degree centrality of BOLD signals between the
groups (9). A significant group by time interaction (t
max
= 4.17, P<
0.005, corrected for cluster size) was found in a single coherent cluster
(k= 35 voxels; voxel size 3 × 3 × 3 mm
3
) extending from the right
superior colliculus of the brainstem [MNI (Montreal Neurological In-
stitute) coordinates: +6, −30, −3] to the bilateral pulvinar nuclei of the
thalamus (MNI coordinates: +6, −18, −3; −6, −21, −3) (Fig. 1). This
interaction was characterized by a significant mean degree centrality
increase in the trained group (t
1,20
=8.55,P< 0.001, d=1.31;pairedttest)
that did not appear in the untrained group, which remained at the base-
line level (t
1,8
= 0.14, P= 0.893; paired ttest) (Fig. 1). To establish the
reliability of the training-induced increase in subcortical network cen-
trality, we performed a confirmatory leave-one-out cross-validation analy-
sis. A linear binary support vector machine classification revealed that
the experimental and control groups are not statistically distinguishable
before the training (accuracy, 54.76%; P=0.272),butdoshowastatisti-
cally significant difference after the training (accuracy, 76.98%; P= 0.017).
Increasing temporal coupling of spontaneous BOLD activity
in the subcortical visual nuclei and the visual cortex
The cluster obtained from the degree centrality analysis was then used
as a seed region in a voxel-wise functional connectivity analysis (10).
1
Department of Neuropsychology, Max Planck Institute for Human Cognitive and
Brain Sciences, Stephanstrasse 1a, 04103 Leipzig, Germany.
2
Centre of Biomedical
Research, Raibareli Road, 226014 Lucknow, Uttar Pradesh, India.
3
University of Hyder-
abad, Prof. C.R. Rao Road, Gachibowli, 500046 Hyderabad, Telangana, India.
4
Centre
for Behavioural and Cognitive Sciences, University of Allahabad, University Road, Old
Katra, 211002 Allahabad, Uttar Pradesh, India.
5
Department of Psychology, University
of Allahabad, 211002 Allahabad, Uttar Pradesh, India.
6
Donders Institute, Radboud
University, Montessorilaan 3, 6525 HR Nijmegen, Netherlands.
7
Psychology of
Language Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1,
6525 XD Nijmegen, Netherlands.
*Corresponding author. Email: skeide@cbs.mpg.de
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Table 1. Participant demographic information and behavioral performance.
Trained group Untrained group Group difference
n21 9 —
Age (years) 31.57 ± 4.90* 31.78 ± 5.47* z= 0.21, P= 0.837
Gender (female/male) 20/1 8/1 —
Monthly income (Rupees) 2313.50 ± 629.15* 2500 ± 433.01* z= 0.96, P= 0.375
Literate family members 2.95 ± 1.54* 2.86 ± 1.46* z=0,P=1
Raven test 13.29 ± 2.67*
†
11.67 ± 2.60*
†
z= 1.42, P= 0.164
Letter knowledge pretest 10.38 ± 12.50*
‡
7.22 ± 10.12*
‡
z= 0.98, P= 0.341
Letter knowledge posttest 33.81 ± 7.11*
‡
5.44 ± 9.84*
‡
z= 4.21, P< 0.001
Word reading pretest 0.57 ± 1.57*
‡
1.56 ± 2.65*
‡
z= 1.41, P= 0.301
Word reading posttest 7.10 ± 8.53*
‡
1.56 ± 2.35*
‡
z= 2.61, P= 0.009
Days between tests 189.76 ± 22.74* 171.22 ± 63.85* z= 1.31, P= 0.193
*Mean ± SD. †Raven test raw scores (maximum 60 points). ‡Raw scores.
Fig. 1. Learning to read modifies subcortical network centrality. Whole-brain degree centrality map thresholded at z= 2.58 (P< 0.005, corrected for cluster size) with
corresponding color bar indicating the range of zscores. The effect of literacy instruction is depicted as a group (reading-trained individuals versus untrained illiterates) by
time (before versus after intervention) interaction. The significant cluster stretches from the right superior colliculus of the brainstem (MNI coordinates: +6, −30, −3) to the
bilateral pulvinar nuclei of the thalamus (MNI coordinates: +6, −18, −3; −6, −21, −3). The box plot resolves the interaction by separately showing the individual mean z
values for each factor level. Mean degree centrality values of the untrained group did not differ significantly from zero (time 1: t
1,8
= 1.76, P= 0.116; time 2: t
1,8
= 1.10, P=
0.302; one-sample ttests).
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OuraimwastoidentifybrainareaswhoseBOLDtimecoursesbecame
more strongly coupled to those of the right superior colliculus and the
bilateral pulvinar nuclei as a consequence of learning to read. A signif-
icant group by time interaction (t
max
= 4.45, P< 0.005, corrected for
cluster size) emerged as a single coherent cluster in the areas V1, V2,
V3, and V4 of the right occipital cortex (k= 48 voxels; voxel size 3 × 3 ×
3mm
3
; MNI coordinates: +24, −81, +15; +24, −93, +12; +33, −90, +3)
(Fig. 2). The cortico-subcortical mean functional connectivity got sig-
nificantly stronger in the group that took part in the reading program
(z=3.77,P< 0.001, r= 0.58; Wilcoxon signed-rank test) but not in
the group that remained illiterate (z= 0.77, P= 0.441; Wilcoxon
signed-rank test).
Stronger functional coactivation in the early visual pathway
and the individual gain in letter and word knowledge
Finally, we wanted to find out whether there was a relation between
the detected neural alterations and the behavioral improvements at
the individual level. To this end, we derived an index for the growth
of brain-functional connectivity [correlation coefficient of the BOLD
time courses of each of the two regions of interest (ROIs) after minus
before the intervention] and two indices for the increase of literacy
(letter knowledge/word-reading skills after minus before the inter-
vention). Individual slopes of cortico-subcortical connectivity were
significantly associated with improvement in letter knowledge (r=
0.40, P= 0.014; one-sided Pearson’s correlation) and with improvement
in word-reading ability (r=0.38,P= 0.018; one-sided Spearman’srank
correlation).
DISCUSSION
We used resting-state fMRI to examine the specific effects of learning
Devanagari script on the functional connectome of illiterate Hindi-
speaking Indian adults within the framework of a controlled longitu-
dinal design. Network centrality of spontaneous hemodynamic activity
significantly increased with training in the bilateral pulvinar nuclei
of the thalamus and the right superior colliculus of the brainstem.
Fig. 2. Learning to read strengthens cortico-subcortical functional connectivity. (A) Voxel-wise functional connectivity map derived from seeding in the significant
degree centrality cluster. The image is thresholded at z= 2.58 (P< 0.005, corrected for cluster size). The color bar indicates the range of zscores. Becoming literate goes
along with increased coupling of BOLD signal time courses between mesencephalic/diencephalic visual nuclei and a single cluster spanning the areas V1, V2, V3, and V4
of the right occipital cortex (MNI coordinates: +24, −81, +15; +24, −93, +12; +33, −90, +3). (B) The group (reading-trained individuals versus untrained illiterates) by time
(before versus after intervention) interaction becomes evident from the box plot, indicating that the functional connectivity is strongly and specifically enhanced in the
group that underwent reading instruction. (C) Line graphs depicting the coefficients of the correlations between the hemodynamic time series separately for each
individual subject, each group, and each time. (D) Mean time series of the BOLD signal for each group and each time.
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Moreover, BOLD signal time courses of these subcortical structures
were significantly more strongly coupled with the areas V1 to V4 of
the right occipital cortex after acquiring basic literacy skills. Individ-
ual gains in intrinsic functional connectivity turned out to be signif-
icantly associated with individual gains in letter identification and
word-reading skills.
Currently existing neurobiological models of reading assume that
literacy boosts low-level hemodynamic responses to complex visual
objects in areas V1 to V4 of the occipital cortex (6). Here, we provide
the first evidence for functional neuroplasticity in mesencephalic and
diencephalic nuclei upstream of V1 as a consequence of reading ac-
quisition. These results call for a reconceptualization of the neural basis
of reading by expanding the experimental perspective from one focused
solely on the cortex to one that also includes the subcortical areas asso-
ciated with oculomotor control and selective visuospatial attention.
Nonhuman primate experiments on visual motion perception sug-
gest that the superior colliculi support the initiation of saccadic eye
movements (11). Accordingly, the observed increase in connectomic
centrality of the right superior colliculus in the course of literacy
training might reflect the fine-tuning of oculomotor activity necessary
for guiding fixations through printed text. An explanation for the
effect in the bilateral pulvinar nuclei can be derived from numerous
studies in humans highlighting the central role of these thalamic
structures for selectively allocating attentional resources to visual
stimuli (12–16). This is in line with several independent studies sug-
gesting a causal role of visuospatial attention skills for reading acqui-
sition. Namely, it has been repeatedly shown in preliterate children
that visuospatial skills predict reading outcome (17,18). Moreover,
there is evidence that reading abilities can be improved by training
with an action video game that challenges visual attention (19).
If interpreted in light of recent nonhuman primate work, enhanced
functional connectivity between the subcortical nuclei and the right oc-
cipital cortex detected after reading intervention indicates that the pul-
vinar is involved in synchronizing information transmission across the
visual cortex (20,21). Signal exchange between these structures is hy-
pothesized to be located anatomically along the long-distance white
matter fiber tract that directly connects them (22,23).
Literacy-driven functional modulations of the right occipital cortex
were not restricted to V1 and V2, as one would expect for alphabetic
writing systems (24), but extended into V3 and V4. This might be ex-
plained by the nature of the Devanagari script, which is visually more
complex than alphabetic writing systems. Devanagari is written from
left to right and used for over 100 languages other than Hindi (for
example, Bengali, Nepali, and Tibetan) and by hundreds of millions
of people. It is an alpha-syllabic writing system comprising the so-
called aksharas that represent sound simultaneously at the syllable
and phoneme level. Vowels and consonants are, thus, not ordered
sequentially as independent letter units inwords. Devanagari is similar
to alphabetic writing systems in that symbols mostly convey a word’s
phonology (that is, distinct units that correspond to individual pho-
nemesratherthansyllablesorwords). However, Devanagari is also sim-
ilar to logographic writing systems (for example, Japanese, Chinese) in
that it also consists of visually complex symbols that are larger than pho-
nological units and that are indivisibleinthatsomeofthecomponent
parts (for example, diacritic signs) cannot stand alone. In line with our
finding in Devanagari, fMRI effects in V3 and V4 during print process-
ing are known from Chinese readers (25). Right-lateralized manifesta-
tions of functional plasticity in the occipital cortex after training
reading-related decoding skills have been repeatedly found especially
in comparable samples of illiterate adults reaching modest performance
levels but remain to be illuminated in future studies (3,4).
Previous task-based fMRI experiments have associated the process
of learning to read with increasing BOLD responses in the so-called
“visual word form area”(VWFA) of the left temporo-occipital fusiform
cortex (2,4). We hypothesize that the high visual processing demands
arising from the complex visuospatial arrangement of Devanagari
characters might have induced a strong training effect in low-level vi-
sual areas (26), and that the potentially more subtle effect of symbolic
learning in the VWFA would not reach statistical significance. Follow-
up studies combining event-related fMRI paradigms with resting-state
fMRI are necessary to confirm this hypothesis. However, we did not
expect to be able to identify the VWFA when seeding in subcortical
nuclei of the visual pathway to examine their resting-state functional
connectivity. The VWFA has been shown repeatedly to be functionally
connected to the dorsal attention network and not to lower-level visual
areas when examining BOLD signals at the low-frequency sampling
range covered in resting-state fMRI (27,28).
Recent cross-sectional MRI studies on adults and school-age chil-
dren have reinvigorated the long-standing view that functional deficits
and structural disruptions of the thalamus might play a role in devel-
opmental dyslexia, the most common learning disorder characterized
by severe difficulties in learning how to read and spell (29–32). Our
results indicate that the functional connectivity profile of the thalamus
can change substantially even after 6 months of reading instruction in
adulthood. Hence, beginning readers appear to train their subcortical
sensory and attentional systems intensively. Therefore, one of the core
challenges for the field is to determine whether thalamic abnormalities
are a potential causal factor for developmental dyslexia or just a con-
sequence of the impoverished reading experience of dyslexic individ-
uals. Recent behavioral work suggests that visual motion processing
skills are causally related to literacy acquisition. Specifically, dyslexic
individuals perform such tasks more poorly than age-matched and
reading level–matched controls (33,34). This could mean that a disrup-
tion of the underlying neural pathway connecting the lateral geniculate
nucleus of the thalamus with V5 might be a contributing cause of dys-
lexia. Whether a similar role can be ascribed to the pathway connecting
the pulvinar nuclei of the thalamus with the occipital cortex must be
determined in follow-up studies. In particular, longitudinal studies
following preschool children are needed to disentangle physiological
causes from consequences arising from impaired literacy acquisition
in scripts carrying both alphabetic and logographic features (35).
Learning-induced changes in coupling of spontaneous functional
responses support the encoding or consolidation of novel experiences
(36–38). Specifically, increased connectivity of functionally distinct
areas might reflect the synchronization of excitability states of different
neuronal populations (39). Future work on animal models combining
resting-state fMRI and electrophysiological recordings is needed to
confirm this hypothesis.
Note that the size of the sample investigated, though comparable
to recent fMRI studies of literacy acquisition (4), is nevertheless small.
Another limitation is that the training effects of the intervention
group were compared with a passive, but not an active, control group.
Accordingly, it remains to be shown whether the results reported here
are literacy-specific or a general effect of visual training involving in-
tricate symbols.
In conclusion, we have shown that only 6 months of learning to
read leads to massive macroscopic functional reorganization processes
in the mature human brain. When becoming literate in adulthood,
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spontaneous hemodynamic activity of mesencephalic and diencephalic
nuclei is strongly coupled with hemodynamic activity of the occipital
cortex. These findings crucially c omplement current neural concepts of
readingbysuggestingthatliteracyexperience reshapes the earliest visual
computation centers even before reaching the primary visual cortex. It
remainstobeshownwhetherdeficitsinthesesubcorticalstructuresare
a consequence of the reduced literacy experience of dyslexic individuals
or a potential cause of their disorder.
MATERIALS AND METHODS
Participants
Participants were recruited from two villages near the city of Lucknow
in the northern Indian state of Uttar Pradesh as part of a study that
was approved by the ethics committee of the Center of Biomedical
Research, Lucknow. After giving informed consent, 51 healthy right-
handed human volunteers without a known history of psychiatric dis-
ease or neurological condition took part in the reading training and in
the resting-state fMRI experiment. For unknown reasons, 18 partici-
pants did not complete the scanning sessions and were therefore ex-
cluded from further analysis (see “Demographic and behavioral data”
for more details). Three additional participants were disregarded be-
cause their fMRI data did not pass our quality control procedure (see
“MRI data”for more details). Accordingly, 30 participants (mean age,
31.63 years; two males; Table 1) were included in the final behavioral
and neural analyses. At the beginning of the study, all of them self-
reported that they were never taught how to read, spell, or write and
never attended school. Subsequently, they were first assessed for their
actual letter (akshara) knowledge and word-reading skills (Table 1) and
then underwent MRI scanning. Not one of them was able to read more
than eight simple words at the beginning of the study. The participants
were randomly assigned either to the group that received reading
instruction (n= 28 at the beginning of the study; n=21includedin
the final analysis) or to the group that did not receive any instruction
(n= 23 at the beginning of the study; n= 9 included in the final analysis).
Final sample sizes were similar to recent fMRI studies of literacy acqui-
sition (4). Group assignment was based on the following order: The first
subject was assigned to the training group, the next subject to the con-
trol group, the third subject to the training group, and so on. For orga-
nizational reasons, all investigators knew the group allocation during
acquisition and analysis of the data. The instructor was a professional
teacher who followed the locally established method of reading
instruction. During the first month of instruction, reading and writing
of the 46 primary Devanagari characters were taught simultaneously.
Thepracticeofaksharas was followed by the practice of two-syllable
words. Approximately 200 words were taught in the first month. Dur-
ing the second month, participants were taught to read and write simple
sentences containing mostly two-syllable words. In the third month of
instruction, the participants started to learn three-syllable words and
continued to practice reading and writing of simple sentences. For
the remaining 3 months of the program, more complex words and
some basic grammar rules were taught. For example, the participants
learned about the differences between nouns, pronouns, verbs, pro-
verbs, and adjectives and also about basic rules of tense and gender.
At the end of the study, that is, approximately 6 months later (mean
gap, 184 days), participants were first scanned and then tested again
on the same day for their akshara letter knowledge and word-reading
skills. The pretest items (used before the intervention) and posttest items
(used after the intervention) were identical. We cannot exclude the pos-
sibility that the participants—as a side effect of literacy—were more fre-
quently exposed to complex pictures (for example, in magazines).
Demographic and behavioral data
Participants were matched for age, gender, handedness, income, num-
ber of literate family members, and nonsymbolic intelligence (Table 1).
Each variable revealed a significant result either in a Kolmogorov-
Smirnov test or in a Shapiro-Wilk test for normality of distribution,
so that nonparametric Mann-Whitney Utests were run to compare
the groups. No significant differences were found for any of the varia-
bles (all z< 1; Table 1). The 18 excluded participants who did not
complete the scanning sessions were significantly younger (z=2.97,
P= 0.003; Mann-Whitney Utest), performed significantly better in
the test of nonsymbolic intelligence (z=2.17,P= 0.030; Mann-
Whitney Utest), and had significantly fewer literate family members
(z=2.54,P= 0.011; Mann-Whitney Utest) compared to the included
30 participants who completed the sessions. The groups showed no
significant difference either in letter knowledge (z=0.47,P= 0.638;
Mann-Whitney Utest) or word-reading (z= 0.62, P= 0.538; Mann-
Whitney Utest) ability at the beginning of the study (see below for
details regarding these measures). Information on age, income, and
number of literate family members was obtained by personal interview.
Right-handedness was also verified in an interview by asking the parti-
cipants which hand they used for common activities (for example,
drawing). Raven’s Progressive Matrices were administered to test for
nonverbal abilities.
Two measures of literacy were taken, namely, letter identification
(knowledge of the 46 primary Devanagari characters) and word-
reading ability (knowledge of 86 words of varying syllabic complexity).
The effects of literacy instruction on behavioral performance were sta-
tistically evaluated using SPSS (www.ibm.com/software/de/analytics/
spss/) to calculate a 2 × 2 mixed-design ANOVA with time [test
performance before the (non-)intervention versus test performance
after the (non-)intervention] as a within-subjects factor and group (il-
literates who underwent intervention versus illiterates who did not
undergo intervention) as a between-subjects factor. ANOVA is an appro-
priate test here because it has been repeatedly demonstrated to yield valid
results independent of the assumption of normality of data distribution
(40,41), which was violated here according to the Kolmogorov-Smirnov
and Shapiro-Wilk tests. Post hoc, nonparametric Wilcoxon signed-rank
tests were run to compute within-subject–level changes in performance.
MRI data
MRI examination was conducted with a 3.0-Tesla Siemens MAGNETOM
Skyra (Siemens AG) whole-body magnetic resonance scanner using a
64–radio frequency–channel head coil.
For anatomical localization, T1-weighted three-dimensional
magnetization-prepared rapid-acquisition gradient echo images were
acquired using a pulse sequence with repetition time (TR) = 1.690 ms,
echo time (TE) = 2.60 ms, inversion time (TI) = 1.100 ms, field of view
(FOV) = 256 × 256, matrix size = 256 × 256 × 192, and voxel size = 1.0 ×
1.0 × 1.0 mm
3
.
For resting-state fMRI (eyes closed, no active stimulation, and no
explicit task), 150 T2*-weighted gradient echo echo-planar imaging
volumes covering 38 slices were collected by applying a pulse sequence
with TR = 2.400 ms, TE = 30 ms, FOV = 224 × 224, matrix size = 64 ×
64×38,andvoxelsize=3.5×3.5×3.0mm
3
.
The T1 images were visually inspected for artifacts and then segmen-
ted into gray matter, white matter, and cerebrospinal fluid using the
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DARTEL algorithm (42) implemented in SPM8 (www.fil.ion.ucl.ac.uk/
spm/software/spm8/). These segmentations served to create individual
tissue masks and a sample-specific template in MNI space.
The fMRI data were preprocessed using the SPM8 software package
(www.fil.ion.ucl.ac.uk/spm/software/spm8/) and the DPARSF toolbox
(www.restfmri.net). First, the first four volumes of each scan were dis-
carded to allow for signal equilibration. Second, the images were slice
time–corrected by interpolation and resampling to the slice at the mid–
time point of each TR. Third, the images were motion-corrected by
realigning them to the first acquired volume. Fourth, additional motion
correction was carried out by regressing out three translational and
three rotational motion parameters of each volume and its preceding
volume as well as the square of each of these values (43). Mean signals
of the white matter and the cerebrospinal fluid and linear and quadratic
trends were also included in this model to control for physiological
noise induced by respiration and pulsating veins. Fifth, each time series
was temporally bandpass-filtered (0.01 to 0.1 Hz) using an ideal rectan-
gular filter. Sixth, the images were resampled to a spatial resolution of
3.0 × 3.0 × 3.0 mm
3
and normalized to the sample-specific template in
MNI space. Finally, the images were spatially smoothed with a 4-mm
full width at half maximum Gaussian kernel, resulting in an
average smoothness of 7.0 × 6.9 × 7.0 mm
3
.
To account for the confounding effect of residual head motion, we
calculated the framewise displacement (FD) of each individual time
series following the approach introduced by Power et al.(44). Of 33
data sets, 30 did not exceed a single-volume threshold of 0.5981 at
both acquisition time points when determining the 100 volumes with
the lowest FD values. The three data sets violating this criterion were
removed fromthe further analyses. The mean FD of the least motion-
distorted 100 volumes included in the final analyses was as low as
0.1036 (SD, 0.0443) for the first time point and 0.1193 (SD, 0.0600)
for the second time point. Of 6000 volumes, 5394 revealed an FD < 0.2.
Whole-brain functional connectivity was computed using the de-
gree centrality algorithm developed by Zuo et al.(9), which quantifies
connectivity by counting the number of correlations of each voxel with
all voxels at a threshold of r> 0.25 and then assigns this number as a
centrality value to each voxel. This analysis was carried out in MNI
space using a group-average gray matter mask of 67.441 voxels. The
resulting degree centrality images were Fisher’sr-to-z–transformed
and then statistically analyzed in the framework of the flexible factorial
design implemented in SPM8 running a 2 × 2 mixed-design ANOVA
with time [test performance before the (non-)intervention versus test
performance after the (non-)intervention] as a within-subjects factor
and group (illiterates who underwent intervention versus illiterates
who did not undergo intervention) as a between-subjects factor. Mean
FD values did not differ significantly within groups between time
points (trained individuals: z=0.92,P= 0.357; untrained illiterates:
z= 0.53, P= 0.594; Wilcoxon signed-rank tests) and also not between
groups (time point 1: z= 0.11, P= 0.934; time point 2: z=1.15,P=
0.263; Mann-Whitney Utests) but were nevertheless entered as a nui-
sance covariate of interest into the ANOVA to remove any potential
relations between residual head motion and the effects of interest (45).
When testing for statistical significance, signal variance of the two
groups was not assumed to be equal because group sizes were different.
Accordingly, Pvalues were Greenhouse-Geisser–corrected to account
for potential nonsphericity of the data. Clusters, that is, connected vox-
els sharing at least a corner (26 voxels), were multiple-comparison–
corrected by combining a type I error threshold of P< 0.005 with a
spatial extent threshold of P< 0.05. The latter threshold was
determined by running 10,000 iterations of a Monte Carlo simulation
as implemented in the AlphaSim tool (http://afni.nimh.nih.gov/),
which revealed a minimum cluster size cutoff of k= 35 voxels (for
the 67.441 gray matter voxels). Note that the size and the smoothness
of the image were determined with SPM8 rather than AlphaSim to
avoid overestimating the level of significance (46). Individual mean z
values of the significant clusters were extracted with the REX toolbox
(https://www.nitrc.org/projects/rex/)andthenplottedseparately
across the factor levels with SPSS to resolve the effects characterizing
the interaction. A confirmatory leave-one-out cross-validation analysis
was carried out by training a linear support vector machine classifier
(with the goal of distinguishing group membership before and after the
training) first on a random subject before quantifying its performance
on the remaining data sets. In accordance with the number of subjects
in the sample, this procedure was repeated 30 times, each time with a
new assignment of subjects and leaving aside each of the already given
observations. Classification performance was estimated by averaging
the indices obtained on the different repetitions. Statistical significance
was determined nonparametrically by running 10,000 iterations of a
permutation test.
The seed-based voxel-wise functional connectivity analysis (10)w
as
carried out by extracting the individual means of the BOLD signal time
series from the significant cluster identified with the degree centrality
approach and then calculating their brain-wide correlation maps,
which were finally Fisher’sr-to-z–transformed. The procedure of
statistical testing was identical to the procedure applied to the degree
centrality maps.
Anatomical identification of all significant clusters was based on the
Harvard-Oxford Subcortical Structural Atlas and the Juelich Histological
Atlas implemented in FSL (47).
Seed-based ROI-wise functional connectivity analyses (10)wererun
by extracting the individual means of the BOLD signal time series from
the two significant clusters obtained from the previous analyses and by
correlating them with each other. Subsequently, the individual correla-
tion coefficients of the BOLD time courses of each of the two ROIs ob-
tained before the (non-)intervention were subtracted from the
coefficients obtained after the (non-)intervention. In addition, the indi-
vidual letter identification and word-reading test scores, respectively,
acquired before the (non-)intervention were subtracted from the scores
obtained after the (non-)intervention. The resulting index of increase of
functional connectivity was correlated separately with the index of in-
crease of letter identification skills (normally distributed data; Pearson’s
product-moment correlation coefficient) and the index of increase of
word-reading performance (not normally distributed data; Spearman’s
rank correlation coefficient) in SPSS. One-sided Pvalues are reported
because the analyses were carried out under the a priori assumption
that better literacy skills would go along with stronger functional
connectivity.
REFERENCES AND NOTES
1. P. H. K. Seymour, M. Aro, J. M. Erskine, Foundation literacy acquisition in European
orthographies. Br. J. Psychol. 94, 143–174 (2003).
2. S. Brem, S. Bach, K. Kucian, J. V. Kujala, T. K. Guttorm, E. Martin, H. Lyytinen, D. Brandeis,
U. Richardson, Brain sensitivity to print emerges when children learn letter-speech
sound correspondences. Proc. Natl. Acad. Sci. U.S.A. 107, 7939–7944 (2010).
3. M. Carreiras, M. L. Seghier, S. Baquero, A. Estévez, A. Lozano, J. T. Devlin, C. J. Price, An
anatomical signature for literacy. Nature 461, 983–986 (2009).
4. S. Dehaene, F. Pegado, L. W. Braga, P. Ventura, G. N. Filho, A. Jobert, G. Dehaene-Lambertz,
R. Kolinsky, J. Morais, L. Cohen, How learning to read changes the cortical networks for
vision and language. Science 330, 1359–1364 (2010).
SCIENCE ADVANCES |RESEARCH ARTICLE
Skeide et al., Sci. Adv. 2017; 3: e1602612 24 May 2017 6of7
on May 25, 2017http://advances.sciencemag.org/Downloaded from
5. E. Striem-Amit, L. Cohen, S. Dehaene, A. Amedi, Reading with sounds: Sensory
substitution selectively activates the visual word form area in the blind. Neuron 76,
640–652 (2012).
6. S. Dehaene, L. Cohen, J. Morais, R. Kolinsky, Illiterate to literate: Behavioural and cerebral
changes induced by reading acquisition. Nat. Rev. Neurosci. 16, 234–244 (2015).
7. M. Carreiras, B. C. Armstrong, M. Perea, R. Frost, The what, when, where, and how of visual
word recognition. Trends Cogn. Sci. 18,90–98 (2014).
8. M. E. Raichle, A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, G. L. Shulman, A
default mode of brain function. Proc. Natl. Acad. Sci. U.S.A. 98, 676–682 (2001).
9. X.-N. Zuo, R. Ehmke, M. Mennes, D. Imperati, F. X. Castellanos, O. Sporns, M. P. Milham,
Network centrality in the human functional connectome. Cereb. Cortex 22, 1862–1875
(2012).
10. B. Biswal, F. Z. Yetkin, V. M. Haughton, J. S. Hyde, Functional connectivity in the motor
cortex of resting human brain using echo-planar mri. Magn. Reson. Med. 34, 537–541
(1995).
11. M. C. Dorris, M. Paré, D. P. Munoz, Neuronal activity in monkey superior colliculus related
to the initiation of saccadic eye movements. J. Neurosci. 17, 8566–8579 (1997).
12. S. Kastner, D. H. O’Connor, M. M. Fukui, H. M. Fehd, U. Herwig, M. A. Pinsk, Functional
imaging of the human lateral geniculate nucleus and pulvinar. J. Neurophysiol. 91,
438–448 (2004).
13. S. Yantis, J. Schwarzbach, J. T. Serences, R. L. Carlson, M. A. Steinmetz, J. J. Pekar,
S. M. Courtney, Transient neural activity in human parietal cortex during spatial attention
shifts. Nat. Neurosci. 5, 995–1002 (2002).
14. J. C. Snow, H. A. Allen, R. D. Rafal, G. W. Humphreys, Impaired attentional selection
following lesions to human pulvinar: Evidence for homology between human
and monkey. Proc. Natl. Acad. Sci. U.S.A. 106, 4054–4059 (2009).
15. A. D. Gouws, I. Alvarez, D. M. Watson, M. Uesaki, J. Rogers, A. B. Morland, On the
role of suppression in spatial attention: Evidence from negative BOLD in
human subcortical and cortical structures. J. Neurosci. 34, 10347–10360 (2014).
16. D. S. Barron, S. B. Eickhoff, M. Clos, P. T. Fox, Human pulvinar functional organization and
connectivity. Hum. Brain Mapp. 36, 2417–2431 (2015).
17. S. Franceschini, S. Gori, M. Ruffino, K. Pedrolli, A. Facoetti, A causal link between visual
spatial attention and reading acquisition. Curr. Biol. 22, 814–819 (2012).
18. J. M. Carroll, J. Solity, L. R. Shapiro, Predicting dyslexia using prereading skills: The role of
sensorimotor and cognitive abilities. J. Child Psychol. Psychiatry 57, 750–758 (2016).
19. S. Franceschini, S. Gori, M. Ruffino, S. Viola, M. Molteni, A. Facoetti, Action video games
make dyslexic children read better. Curr. Biol. 23, 462–466 (2013).
20. Y. B. Saalmann, S. Kastner, Cognitive and perceptual functions of the visual thalamus.
Neuron 71, 209–223 (2011).
21. Y. B. Saalmann, M. A. Pinsk, L. Wang, X. Li, S. Kastner, The pulvinar regulates information
transmission between cortical areas based on attention demands. Science 337, 753–756
(2012).
22. S. E. Leh, M. M. Chakravarty, A. Ptito, The connectivity of the human pulvinar: A diffusion
tensor imaging tractography study. Int. J. Biomed. Imaging 2008, 789539 (2008).
23. M. J. Arcaro, M. A. Pinsk, S. Kastner, The anatomical and functional organization of the
human visual pulvinar. J. Neurosci. 35, 9848–9871 (2015).
24. M. Sigman, H. Pan, Y. Yang, E. Stern, D. Silbersweig, C. D. Gilbert, Top-down
reorganization of activity in the visual pathway after learning a shape identification task.
Neuron 46, 823–835 (2005).
25. M. Szwed, E. Qiao, A. Jobert, S. Dehaene, L. Cohen, Effects of literacy in early visual and
occipitotemporal areas of Chinese and French readers. J. Cogn. Neurosci. 26, 459–475
(2014).
26. N. C. Singh, C. Rao, Reading in Devanagari: Insights from functional neuroimaging. Indian
J. Radiol. Imaging 24,44–50 (2014).
27. A. C. Vogel, F. M. Miezin, S. E. Petersen, B. L. Schlaggar, The putative visual word form area
is functionally connected to the dorsal attention network. Cereb. Cortex 22, 537–549
(2012).
28. X. Wang, A. Caramazza, M. V. Peelen, Z. Han, Y. Bi, Reading without speech sounds: VWFA
and its connectivity in the congenitally deaf. Cereb. Cortex 25, 2416–2426 (2015).
29. K. Jednoróg, A. Marchewka, I. Altarelli, A. K. M. Lopez, M. van Ermingen-Marbach,
M. Grande, A. Grabowska, S. Heim, F. Ramus, How reliable are gray matter disruptions
in specific reading disability across multiple countries and languages? Insights from
a large-scale voxel-based morphometry study. Hum. Brain Mapp. 36, 1741–1754 (2015).
30. B. Diaz, F. Hintz, S. J. Kiebel, K. von Kriegstein, Dysfunction of the auditory thalamus in
developmental dyslexia. Proc. Natl. Acad. Sci. U.S.A. 109, 13841–13846 (2012).
31. M. S. Livingstone, G. D. Rosen, F. W. Drislane, A. M. Galaburda, Physiological and
anatomical evidence for a magnocellular defect in developmental dyslexia. Proc. Natl.
Acad. Sci. U.S.A. 88, 7943–7947 (1991).
32. J. Stein, The magnocellular theory of developmental dyslexia. Dyslexia 7,12–36 (2001).
33. S. Gori, A. R. Seitz, L. Ronconi, S. Franceschini, A. Facoetti, Multiple causal links between
magnocellular-dorsal pathway deficit and developmental dyslexia. Cereb. Cortex 26,
4356–4369 (2016).
34. S. Gori, P. Cecchini, A. Bigoni, M. Molteni, A. Facoetti, Magnocellular-dorsal pathway and
sub-lexical route in developmental dyslexia. Front. Hum. Neurosci. 8, 460 (2014).
35. U. Goswami, Sensory theories of developmental dyslexia: Three challenges for research.
Nat. Rev. Neurosci. 16,43–54 (2015).
36. C. M. Lewis, A. Baldassarre, G. Committeri, G. L. Romani, M. Corbetta, Learning sculpts the
spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. U.S.A. 106,
17558–17563 (2009).
37. A. Baldassarre, C. M. Lewis, G. Committeri, A. Z. Snyder, G. L. Romani, M. Corbetta,
Individual variability in functional connectivity predicts performance of a perceptual task.
Proc. Natl. Acad. Sci. U.S.A. 109, 3516–3521 (2012).
38. M. D. Gregory, Y. Agam, C. Selvadurai, A. Nagy, M. Vangel, M. Tucker, E. M. Robertson,
R. Stickgold, D. S. Manoach, Resting state connectivity immediately following learning
correlates with subsequent sleep-dependent enhancement of motor task performance.
NeuroImage 102 (Pt. 2), 666–673 (2014).
39. T. Harmelech, R. Malach, Neurocognitive biases and the patterns of spontaneous
correlations in the human cortex. Trends Cogn. Sci. 17, 606–615 (2013).
40. M. R. Harwell, E. N. Rubinstein, W. S. Hayes, C. C. Olds, Summarizing Monte Carlo results
in methodological research: The one-factor and two-factor fixed effects ANOVA cases.
J. Educ. Behav. Stat. 17, 315–339 (1992).
41. L. M. Lix, J. C. Keselman, H. J. Keselman, Consequences of assumption violations
revisited: A quantitative review of alternatives to the one-way analysis of variance Ftest.
Rev. Educ. Res. 66, 579–619 (1996).
42. J. Ashburner, A fast diffeomorphic image registration algorithm. NeuroImage 38,95–113
(2007).
43. K. J. Friston, S. Williams, R. Howard, R. S. Frackowiak, R. Turner, Movement-related effects
in fMRI time-series. Magn. Reson. Med. 35, 346–355 (1996).
44. J. D. Power, K. A. Barnes, A. Z. Snyder, B. L. Schlaggar, S. E. Petersen, Spurious
but systematic correlations in functional connectivity MRI networks arise from subject
motion. NeuroImage 59, 2142–2154 (2012).
45. C.-G. Yan, B. Cheung, C. Kelly, S. Colcombe, R. C. Craddock, A. Di Martino, Q. Li, X.-N. Zuo,
F. X. Castellanos, M. P. Milham, A comprehensive assessment of regional variation in the
impact of head micromovements on functional connectomics. NeuroImage 76,183–201
(2013).
46. A. Eklund, T. E. Nichols, H. Knutsson, Cluster failure: Why fMRI inferences for spatial extent
have inflated false-positive rates. Proc. Natl. Acad. Sci. U.S.A. 113, 7900–7905 (2016).
47. M. Jenkinson, C. F. Beckmann, T. E. J. Behrens, M. W. Woolrich, S. M. Smith, FSL.
NeuroImage 62,782–790 (2012).
Acknowledgments: F.H. would like to thank A. Cutler. Funding: This project was funded by
the Max Planck Society. Author contributions: F.H. designed the research; U.K., R.K.M., V.N.T.,
A.G., and J.P.S. recruited the participants and collected data; M.A.S. performed the analyses;
and M.A.S., F.E., and F.H. wrote the paper. Competing interests: The authors declare that they
have no competing interests. Data and materials availability: All data needed to evaluate
the conclusions in the paper are present in the paper. Additional data related to this paper may
be requested from F.H.
Submitted 22 October 2016
Accepted 23 March 2017
Published 24 May 2017
10.1126/sciadv.1602612
Citation: M. A. Skeide, U. Kumar, R. K. Mishra, V. N. Tripathi, A. Guleria, J. P. Singh, F. Eisner,
F. Huettig, Learning to read alters cortico-subcortical cross-talk in the visual system of
illiterates. Sci. Adv. 3, e1602612 (2017).
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on May 25, 2017http://advances.sciencemag.org/Downloaded from
doi: 10.1126/sciadv.1602612
2017, 3:.Sci Adv
Huettig (May 24, 2017)
Tripathi, Anupam Guleria, Jay P. Singh, Frank Eisner and Falk
Michael A. Skeide, Uttam Kumar, Ramesh K. Mishra, Viveka N.
visual system of illiterates
Learning to read alters cortico-subcortical cross-talk in the
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