According to most textbooks, our brain is divided into separate areas that are dedicated to specific senses. We have a visual cortex for vision, a tactile cortex for touch, and so on. However, researchers suspect that this division might not be as fixed as the textbooks say. For example, blind people can switch their 'leftover' visual cortex to non-visual purposes, such as reading Braille – a tactile alphabet.

Can this switch in functional organization also happen in healthy people with normal vision? To investigate this, Siuda-Krzywicka, Bola et al. taught a group of healthy, sighted people to read Braille by touch, and monitored the changes in brain activity that this caused using a technique called functional magnetic resonance imaging. According to textbooks, tactile reading should engage the tactile cortex. Yet, the experiment revealed that the brain activity critical for reading Braille by touch did not occur in the volunteers’ tactile cortex, but in their visual cortex.

Further experiments used a technique called transcranial magnetic stimulation to suppress the activity of the visual cortex of the volunteers. This impaired their ability to read Braille by touch. This is a clear-cut proof that sighted adults can re-program their visual cortex for non-visual, tactile purposes.

These results show that intensive training in a complex task can overcome the sensory division-of-labor of our brain. This indicates that our brain is much more flexible than previously thought, and that such flexibility might occur when we learn everyday, complex skills such as driving a car or playing a musical instrument.

The next question that follows from this work is: what enables the brain’s activity to change after learning to read Braille? To understand this, Siuda-Krzywicka, Bola et al. are currently exploring how the physical structure of the brain changes as a result of a person acquiring the ability to read Braille by touch.

The current view of neural plasticity in the adult brain sees it as ubiquitous but generally constrained by functional boundaries (Hoffman and Logothetis, 2009). At the systems level, experience-driven plasticity is thought to operate within the limits of sensory divisions, where the visual cortex processes visual stimuli and responds to visual training, the tactile cortex processes tactile stimuli and responds to tactile training, and so on. A departure from this rule is usually reported only during large-scale reorganization induced by sensory loss or injury (Hirsch et al., 2015; Lomber et al., 2011; Merabet and Pascual-Leone, 2010; Pavani and Roder, 2012). The ventral visual cortex, in particular, is activated in blind subjects who read Braille (Büchel et al., 1998; Reich et al., 2011; Sadato et al., 2002, 1996) and lesions of this area impair Braille reading (Hamilton et al., 2000). This visual cortex thus has the innate connectivity required to carry out a complex perceptual task – reading – in a modality different than vision. It is unclear, however, to what extent this connectivity is preserved and functional in the normal, adult brain.

A growing amount of evidence suggests that some form of cross-modal recruitment of the visual cortex could be possible in the normal healthy adults (Amedi et al., 2007; Kim and Zatorre, 2011; Powers et al., 2012; Saito et al., 2006; Zangenehpour and Zatorre, 2010; Merabet et al., 2004; Zangaladze et al., 1999). Nonetheless, the behavioural relevance of these cortical mechanisms remains unclear, especially for complex stimuli. Notably, several experiments failed to find such cross-modal reorganization in sighted subjects, even after extensive training (Kupers et al., 2006; Ptito et al., 2005). One study found cross-modal plastic changes in subjects that were blindfolded for several days (Merabet et al., 2008), but these plastic changes quickly vanished once the subjects removed their blindfolds.

Here, for the first time, we show that large-scale, cross-modal cortical reorganization is a viable, adaptive mechanism in the sighted, adult brain. In our experiment, sighted adults followed a 9-month Braille reading course. The resulting cortical changes were tracked using task-based and resting-state functional Magnetic Resonance Imaging (fMRI) and manipulated with Transcranial Magnetic Stimulation (TMS).

Twenty-nine sighted adults, Braille teachers and professionals, students specializing in the education of the blind, and family members of blind people, took part in the study. Some subjects were familiar with Braille signs visually (some teachers actually knew how to read visual Braille upside-down, as upside-down was the usual orientation at which they viewed their students’ work laid out on school benches). All of them were naive in tactile Braille reading (Appendix 1.1). All the participants completed an intensive, tailor-made, 9-month-long tactile Braille reading course (Materials and methods). The majority progressed significantly in tactile reading, reaching an average performance of 6.20 words per minute read aloud (WPM) (SD=3.94, range = 0–17, Appendix 1.1) at the end of the course. Because the course curriculum included learning to recognize Braille letters by sight, the subjects also improved in reading Braille characters visually (Appendix 1.2).

Before and after the tactile Braille reading course, the subjects took part in an fMRI experiment (Figure 1A–C; 'Materials and methods'), in which they viewed regular visual words, Braille words displayed visually on a screen (visual Braille words) and touched tactile Braille words. Additional control conditions included visual strings of hash symbols (control for visual words), visual strings of pseudo-Braille characters (control for visual Braille), and tactile strings of pseudo-Braille characters (control for tactile Braille) (Figure 1B and 'Materials and methods'; a pseudo-Braille character contains six dots and has no meaning). The subjects could not see any of the tactile stimuli. Subjects also performed a control mental imagery fMRI experiment ('Materials and methods', Figure 1—figure supplements 1 and 2, Appendix 1.5).

Figure 1 with 3 supplements see all

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Representation similarity analysis

Visual words, visual Braille and tactile Braille all elicited activity in the VWFA. How similar are the neural representations of these three scripts? The similarity of neural representations can be studied with multivariate pattern analysis-representational similarity analysis (MVPA-RSA). This method is based on the premise that stimuli that share similar neural representations will generate similar voxel activation patterns (Kriegeskorte et al., 2008; Rothlein and Rapp, 2014). Using MVPA-RSA ('Materials and methods'), we found that the two Braille conditions (visual and tactile) had the most similar activation patterns in the VWFA (Figure 2A,r=0.48), despite very large differences in the magnitude of the two activations (see Figure 2B). The correlations of the two Braille conditions with visual words were much weaker (Figure 2A, 'Materials and methods' and Appendix 1.6). Despite a difference in modality (visual vs. tactile), the neural representations of the two versions of Braille script were partially similar and distinct from the well-established representation for visual words.

Figure 2

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Effects on resting-state fMRI

Learning can impact the resting-state activity of the brain (e.g. Lewis et al., 2009). Following the course, we observed an increase in resting-state functional connectivity between the VWFA seed and the left primary somatosensory cortex (Figure 3A, red; p<0.001, corrected for multiple comparisons; Z=4.57; MNI: -57 -24 45; 'Materials and methods'). This increase was the only statistically significant positive effect found. The same comparison showed a decrease in the VWFA’s functional connectivity with other visual areas, bilaterally. Furthermore, after the course, the VWFA-S1 functional connectivity level was correlated with the subjects’ progress in tactile Braille reading speed (in the month preceding the after-course scan, Figure 3; r(27)=0.49, p=0.007). The VWFA thus increased its coupling with the somatosensory cortex while decreasing its coupling with other visual areas. This VWFA-S1 functional connectivity was behaviorally relevant for tactile reading and was likely to be dynamically modulated in a relatively short learning period (similar to: Lewis et al., 2009) (see also Appendix 1.7).

Figure 3

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Transcranial magnetic stimulation

Finally, to test the role of the VWFA in Braille reading, we performed a repetitive Transcranial Magnetic Stimulation (rTMS) experiment in which nine subjects were tested after the course in tactile Braille reading (reading speeds: 3–17 WPM). rTMS was applied to the VWFA and to two control sites – the lateral occipital area and the vertex. Both the VWFA and the lateral occipital area were localized using the individual subjects’ fMRI results ('Materials and methods'). Similar to a previous visual reading study (Duncan et al., 2010), we chose the lateral occipital area as an additional, negative control site, because TMS to this region evokes muscle contractions indistinguishable from VWFA stimulation. rTMS was applied while subjects performed a lexical decision task on words and pseudowords written in tactile Braille (see Figure 4A; 'Materials and methods'). Based on a previous visual reading study (Duncan et al., 2010), we expected that stimulation to the VWFA during the performance of the task would decrease accuracy for lexical decisions on Braille words. As predicted, TMS to the VWFA decreased the accuracy of reading Braille words (t(8)=3.02, p=0.016, Figure 4B). This TMS result shows that the VWFA is necessary for reading tactile Braille words.

Figure 4

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We also tested for effects of TMS on control stimuli (Braille pseudowords) and control sites (lateral occipital area, vertex) (Figure 4B) and none of these tests resulted in significant effects (VWFA, Braille pseudowords: t(8)=0.03, p=0.977; lateral occipital area, Braille words: t(8)=0.18, p=0.859; lateral occipital area, Braille pseudowords: t(8)=0.03, p=0.979; vertex, Braille words: t(8)=1.26, p=0.243; vertex, Braille pseudowords: t(8)=0.02, p=0.986). However, given the small number of subjects in this part of our study, our TMS experiment was underpowered to statistically verify the specificity of the TMS effect: ANOVAs testing for specificity of our effect of interest showed a trend for an interaction between the stimulus type and TMS for the VWFA (F(1,8)=3.59, p=0.095), no interaction between the stimulus type, TMS, and the stimulation site (F(2,16)=0.41, p=0.580), and no interaction between TMS and the stimulation site for Braille words (F(2,16)=1.5, p=0.253).

Several experiments have already indicated that in some contexts, the sighted’s ventral visual cortex can contribute to the perception of tactile (reviewed in: Amedi et al., 2005) or auditory (Amedi et al., 2007) stimuli. It is also known that regions higher up in the sensory processing hierarchy, notably the antero-medial parts of the ventral temporal cortex (MNI y>-40), can host multisensory, abstract object representations (e.g. Fairhall and Caramazza, 2013; Kassuba et al., 2014). The left fusiform gyrus in particular is suggested to process object-specific crossmodal interactions (Kassuba et al., 2011). Our results demonstrate that the occipitotemporal visual cortex (VWFA, MNI y≈-60) can represent stimuli in a modality other than vision. The fact that TMS to the VWFA can disrupt tactile reading demonstrates the importance of this representation for sensory processing.

ROI analysis (Figure 2B) revealed that lateral occipital area (LOA) presented a pattern of activity increase to tactile words due to the course similar to the VWFA. However, TMS applied to LOA did not disturb tactile reading process (Figure 4). While the LOA is activated in various visual word recognition tasks (Duncan et al., 2009; Wright et al., 2008), its lesions seems not to affect reading itself (Cavina-Pratesi et al., 2015; Milner et al., 1991; Philipose et al., 2007). The increase of activity in both LOA and VWFA for tactile reading thus suggest that visual and tactile reading share similar neural correlates along the ventral visual stream. However, the exact function of LOA in reading seems to be more accessory than critical.

The ventral visual cortex was also activated when subjects heard auditory words and then imagined reading them in tactile Braille (Figure 1—figure supplement 2, Table 4). We also observed robust object activations in the object imagery task (ventral visual stream, left: MNI -45 -67 -9, Z=6.68, right: MNI 45 -67 -5, Z=5.94) which confirms that subjects successfully engaged in imagery during the experiment.

There are several reasons to rule out the possibility that the visual activation for tactile reading is a side-effect of mental imagery. First, if the visual activations were only a by-product of mental imagery, TMS to the VWFA would not interfere with Braille reading; yet, it did (Figure 4B). Second, training did not produce any changes in the activations to imagining tactile Braille (Appendix 1.5). Third, activations to imagining Braille in the VWFA did not correlate with tactile Braille reading proficiency (Appendix 1.4). Thus, visual cortex activations for tactile reading cannot be explained as a by-product of visual imagery. Instead, they constitute the signature of a new, tactile script representation emerging in the visual cortex.

Cortical reorganization that crosses the sensory boundaries is prominent in blind and deaf humans (Hirsch et al., 2015; Pavani and Roder, 2012; Sadato et al., 2002) and in congenitally deaf cats (Lomber et al., 2011). In the latter, the deprived auditory areas play a vital role in the deaf cats’ superior visual perception. The above-mentioned studies used the same paradigms in non-deprived humans and cats, but did not find any signs of cross-modal reorganization (e.g. Figure 7 in Sadato et al., 2002). One could have thus expected that learning to read by touch would lead to the emergence of a tactile reading area in the somatosensory cortex. Yet, our study produced a different result. Specific responses to tactile reading emerged not in somatosensory areas, but in visual areas.

This result might seem incompatible with a large body of data showing that tactile training leads to changes in somatosensory activation to tactile stimuli (Guic et al., 2008; Kupers et al., 2006; Pleger et al., 2003; Ptito et al., 2005), or decision-level frontal cortex changes (Sathian et al., 2013), but no visual cortex changes. However, all the above-mentioned experiments used simple stimuli, such as gratings, and short learning periods. Our experiment was longer (9 months) and used complex stimuli - entire Braille words. Experiments that study learning-related plasticity at multiple time points (Lövdén et al., 2013) suggest that at the initial stage of Braille learning, the somatosensory cortex might have increased its response to Braille words. Then, as the effects of early sensory learning consolidated in the somatosensory cortex, the cortical focus of learning shifted elsewhere, in our case the ventral visual stream.

Previous studies (Büchel et al., 1998; Lomber et al., 2011; Merabet and Pascual-Leone, 2010; Sadato et al., 2002, 1996) have suggested that cross-modal plasticity is possible mainly as a result of massive sensory deprivation or injury. Our results demonstrate that given a long training period and a complex task, the normal brain is capable of large-scale plasticity that overcomes the division into separate sensory cortices. The exact mechanisms of this plasticity might of course be different in deprived and non-deprived subjects.

Earlier reports of cross-modal plasticity in the normal brain might have already hinted at the possibility of cross-modal plasticity in the normal brain. Indeed, it was shown that some parts of the visual cortex can be activated during auditory or tactile tasks (Amedi et al., 2007; Kim and Zatorre, 2011; Powers et al., 2012; Saito et al., 2006; Zangenehpour and Zatorre, 2010). Professional pianists, for example, were shown to activate their auditory cortex when viewing mute video recordings of a piano key being pressed (Haslinger et al., 2005). The behavioral relevance of such activations, however, was demonstrated only for simple tactile stimuli: grating orientation (Zangaladze et al., 1999) and distance judgment (Merabet et al., 2004). Our study used a controlled, within-subject design and precise behavioral measures supplemented with a causal method, TMS. Its results suggest that large-scale plasticity is a viable mechanism recruited when learning complex skills. This conclusion is congruent with a recent comparative study showing that the morphology of cerebral cortex is substantially less genetically heritable in humans than in chimpanzees (Gomez-Robles et al., 2015). Such relaxed genetic control might be the reason for homo sapiens’ increased learning abilities and greater behavioral flexibility.

Despite evidence for cross-modal plasticity of the human brain, the dominant view still describes it as necessarily constrained by the sensory boundaries (e.g. Figure 18–2 in: Kandel et al., 2012). Our study provides a clear-cut evidence that learning-related neuroplasticity can overcome the sensory division. This calls for a re-assessment of our view of the functional organization of the brain.

1. 1. Amedi A
   2. Malach R
   3. Hendler T
   4. Peled S
   5. Zohary E

   (2001) Visuo-haptic object-related activation in the ventral visual pathway

   Nature Neuroscience 4:324–330.

   https://doi.org/10.1038/85201

   • Google Scholar
2. 1. Amedi A
   2. Merabet LB
   3. Bermpohl F
   4. Pascual-Leone A

   (2005) The occipital cortex in the blind. lessons about plasticity and vision

   Current Directions in Psychological Science 14:306–311.

   https://doi.org/10.1111/j.0963-7214.2005.00387.x

   • Google Scholar
3. 1. Amedi A
   2. Stern WM
   3. Camprodon JA
   4. Bermpohl F
   5. Merabet L
   6. Rotman S
   7. Hemond C
   8. Meijer P
   9. Pascual-Leone A

   (2007) Shape conveyed by visual-to-auditory sensory substitution activates the lateral occipital complex

   Nature Neuroscience 10:687–689.

   https://doi.org/10.1038/nn1912

   • Google Scholar
4. 1. Andrews-Hanna JR
   2. Snyder AZ
   3. Vincent JL
   4. Lustig C
   5. Head D
   6. Raichle ME
   7. Buckner RL

   (2007) Disruption of large-scale brain systems in advanced aging

   Neuron 56:924–935.

   https://doi.org/10.1016/j.neuron.2007.10.038

   • Google Scholar
5. 1. Büchel C
   2. Price C
   3. Friston K

   (1998) A multimodal language region in the ventral visual pathway

   Nature 394:274–277.

   https://doi.org/10.1038/28389

   • Google Scholar
6. 1. Cavina-Pratesi C
   2. Large ME
   3. Milner AD

   (2015) Reprint of: visual processing of words in a patient with visual form agnosia: a behavioural and fMRI study

   Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 72:97–114.

   https://doi.org/10.1016/j.cortex.2015.10.001

   • Google Scholar
7. 1. Chao-Gan Y
   2. Yu-Feng Z

   (2010) DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI

   Frontiers in Systems Neuroscience 4:.

   https://doi.org/10.3389/fnsys.2010.00013

   • Google Scholar
8. 1. Cohen L
   2. Lehéricy S
   3. Chochon F
   4. Lemer C
   5. Rivaud S
   6. Dehaene S

   (2002)

   Language-specific tuning of visual cortex? functional properties of the visual word form area

   Brain 125:1054–1069.

   • Google Scholar
9. 1. Dehaene S
   2. Cohen L

   (2011) The unique role of the visual word form area in reading

   Trends in Cognitive Sciences 15:254–262.

   https://doi.org/10.1016/j.tics.2011.04.003

   • Google Scholar
10. 1. Dienes Z

    (2014) Using bayes to get the most out of non-significant results

    Frontiers in Psychology 5:.

    https://doi.org/10.3389/fpsyg.2014.00781

    • Google Scholar
11. 1. Duncan KJ
    2. Pattamadilok C
    3. Knierim I
    4. Devlin JT

    (2009) Consistency and variability in functional localisers

    NeuroImage 46:1018–1026.

    https://doi.org/10.1016/j.neuroimage.2009.03.014

    • Google Scholar
12. 1. Duncan KJ
    2. Pattamadilok C
    3. Devlin JT

    (2010) Investigating occipito-temporal contributions to reading with TMS

    Journal of Cognitive Neuroscience 22:739–750.

    https://doi.org/10.1162/jocn.2009.21207

    • Google Scholar
13. 1. Eickhoff SB
    2. Weiss PH
    3. Amunts K
    4. Fink GR
    5. Zilles K

    (2006) Identifying human parieto-insular vestibular cortex using fMRI and cytoarchitectonic mapping

    Human Brain Mapping 27:611–621.

    https://doi.org/10.1002/hbm.20205

    • Google Scholar
14. 1. Fairhall SL
    2. Caramazza A

    (2013) Brain regions that represent amodal conceptual knowledge

    Journal of Neuroscience 33:10552–10558.

    https://doi.org/10.1523/JNEUROSCI.0051-13.2013

    • Google Scholar
15. 1. Fox MD
    2. Zhang D
    3. Snyder AZ
    4. Raichle ME

    (2009) The global signal and observed anticorrelated resting state brain networks

    Journal of Neurophysiology 101:3270–3283.

    https://doi.org/10.1152/jn.90777.2008

    • Google Scholar
16. 1. Friston KJ
    2. Holmes AP
    3. Poline JB
    4. Grasby PJ
    5. Williams SC
    6. Frackowiak RS
    7. Turner R

    (1995) Analysis of fMRI time-series revisited

    NeuroImage 2:45–53.

    https://doi.org/10.1006/nimg.1995.1007

    • Google Scholar
17. 1. Friston KJ
    2. Williams S
    3. Howard R
    4. Frackowiak RS
    5. Turner R

    (1996)

    Movement-related effects in fMRI time-series

    Magnetic Resonance in Medicine 35:346–355.

    • Google Scholar
18. 1. Gómez-Robles A
    2. Hopkins WD
    3. Schapiro SJ
    4. Sherwood CC

    (2015) Relaxed genetic control of cortical organization in human brains compared with chimpanzees

    Proceedings of the National Academy of Sciences of the United States of America 112:14799–14804.

    https://doi.org/10.1073/pnas.1512646112

    • Google Scholar
19. 1. Gough PM
    2. Nobre AC
    3. Devlin JT

    (2005) Dissociating linguistic processes in the left inferior frontal cortex with transcranial magnetic stimulation

    The Journal of Neuroscience 25:8010–8016.

    https://doi.org/10.1523/JNEUROSCI.2307-05.2005

    • Google Scholar
20. 1. Guic E
    2. Carrasco X
    3. Rodríguez E
    4. Robles I
    5. Merzenich MM

    (2008)

    Plasticity in primary somatosensory cortex resulting from environmentally enriched stimulation and sensory discrimination training

    Biological Research 41:425–437.

    • Google Scholar
21. 1. Göbel S
    2. Walsh V
    3. Rushworth MF

    (2001) The mental number line and the human angular gyrus

    NeuroImage 14:1278–1289.

    https://doi.org/10.1006/nimg.2001.0927

    • Google Scholar
22. 1. Hallquist MN
    2. Hwang K
    3. Luna B

    (2013) The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity

    NeuroImage 82:208–225.

    https://doi.org/10.1016/j.neuroimage.2013.05.116

    • Google Scholar
23. 1. Hamilton R
    2. Keenan JP
    3. Catala M
    4. Pascual-Leone A

    (2000)

    Alexia for braille following bilateral occipital stroke in an early blind woman

    Neuroreport 11:237–240.

    • Google Scholar
24. 1. Haslinger B
    2. Erhard P
    3. Altenmüller E
    4. Schroeder U
    5. Boecker H
    6. Ceballos-Baumann AO

    (2005) Transmodal sensorimotor networks during action observation in professional pianists

    Journal of Cognitive Neuroscience 17:282–293.

    https://doi.org/10.1162/0898929053124893

    • Google Scholar
25. 1. Hirsch GV
    2. Corinna MB
    3. Merabet LB

    (2015)

    Using structural and functional brain imaging to uncover how the brain adapts to blindness

    Annals of Neuroscience and Psychology 2:.

    • Google Scholar
26. 1. Hoffman KL
    2. Logothetis NK

    (2009) Cortical mechanisms of sensory learning and object recognition

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 364:321–329.

    https://doi.org/10.1098/rstb.2008.0271

    • Google Scholar
27. 1. Kassuba T
    2. Klinge C
    3. Hölig C
    4. Menz MM
    5. Ptito M
    6. Röder B
    7. Siebner HR

    (2011) The left fusiform gyrus hosts trisensory representations of manipulable objects

    NeuroImage 56:1566–1577.

    https://doi.org/10.1016/j.neuroimage.2011.02.032

    • Google Scholar
28. 1. Kassuba T
    2. Klinge C
    3. Hölig C
    4. Röder B
    5. Siebner HR

    (2014) Short-term plasticity of visuo-haptic object recognition

    Frontiers in Psychology 5:.

    https://doi.org/10.3389/fpsyg.2014.00274

    • Google Scholar
29. 1. Kim JK
    2. Zatorre RJ

    (2011) Tactile-auditory shape learning engages the lateral occipital complex

    The Journal of Neuroscience 31:7848–7856.

    https://doi.org/10.1523/JNEUROSCI.3399-10.2011

    • Google Scholar
30. 1. Kriegeskorte N
    2. Mur M
    3. Bandettini P

    (2008) Representational similarity analysis - connecting the branches of systems neuroscience

    Frontiers in Systems Neuroscience 2:.

    https://doi.org/10.3389/neuro.06.004.2008

    • Google Scholar
31. 1. Kriegeskorte N
    2. Simmons WK
    3. Bellgowan PS
    4. Baker CI

    (2009) Circular analysis in systems neuroscience: the dangers of double dipping

    Nature Neuroscience 12:535–540.

    https://doi.org/10.1038/nn.2303

    • Google Scholar
32. 1. Kupers R
    2. Fumal A
    3. de Noordhout AM
    4. Gjedde A
    5. Schoenen J
    6. Ptito M

    (2006) Transcranial magnetic stimulation of the visual cortex induces somatotopically organized qualia in blind subjects

    Proceedings of the National Academy of Sciences of the United States of America 103:13256–13260.

    https://doi.org/10.1073/pnas.0602925103

    • Google Scholar
33. 1. Lewis CM
    2. Baldassarre A
    3. Committeri G
    4. Romani GL
    5. Corbetta M

    (2009) Learning sculpts the spontaneous activity of the resting human brain

    Proceedings of the National Academy of Sciences of the United States of America 106:17558–17563.

    https://doi.org/10.1073/pnas.0902455106

    • Google Scholar
34. 1. Lomber SG
    2. Meredith MA
    3. Kral A

    (2011) Adaptive crossmodal plasticity in deaf auditory cortex: areal and laminar contributions to supranormal vision in the deaf

    Progress in Brain Research 191:251–270.

    https://doi.org/10.1016/B978-0-444-53752-2.00001-1

    • Google Scholar
35. 1. Lotze M
    2. Halsband U

    (2006) Motor imagery

    Journal of Physiology, Paris 99:386–395.

    https://doi.org/10.1016/j.jphysparis.2006.03.012

    • Google Scholar
36. 1. Lövdén M
    2. Wenger E
    3. Mårtensson J
    4. Lindenberger U
    5. Bäckman L

    (2013) Structural brain plasticity in adult learning and development

    Neuroscience and Biobehavioral Reviews 37:2296–2310.

    https://doi.org/10.1016/j.neubiorev.2013.02.014

    • Google Scholar
37. 1. Mandera P
    2. Keuleers E
    3. Wodniecka Z
    4. Brysbaert M

    (2014) Subtlex-pl: subtitle-based word frequency estimates for polish

    Behavior Research Methods 47:471–483.

    https://doi.org/10.3758/s13428-014-0489-4

    • Google Scholar
38. 1. Merabet L
    2. Thut G
    3. Murray B
    4. Andrews J
    5. Hsiao S
    6. Pascual-Leone A

    (2004)

    Feeling by sight or seeing by touch?

    Neuron 42:173–179.

    • Google Scholar
39. 1. Merabet LB
    2. Hamilton R
    3. Schlaug G
    4. Swisher JD
    5. Kiriakopoulos ET
    6. Pitskel NB
    7. Kauffman T
    8. Pascual-Leone A

    (2008) Rapid and reversible recruitment of early visual cortex for touch

    PloS One 3:e3046.

    https://doi.org/10.1371/journal.pone.0003046

    • Google Scholar
40. 1. Merabet LB
    2. Pascual-Leone A

    (2010) Neural reorganization following sensory loss: the opportunity of change

    Nature Reviews. Neuroscience 11:44–52.

    https://doi.org/10.1038/nrn2758

    • Google Scholar
41. 1. Milner AD
    2. Perrett DI
    3. Johnston RS
    4. Benson PJ
    5. Jordan TR
    6. Heeley DW
    7. Bettucci D
    8. Mortara F
    9. Mutani R
    10. Terazzi E

    (1991)

    Perception and action in 'visual form agnosia'

    Brain 114:405–428.

    • Google Scholar
42. 1. Murphy K
    2. Birn RM
    3. Handwerker DA
    4. Jones TB
    5. Bandettini PA

    (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

    NeuroImage 44:893–905.

    https://doi.org/10.1016/j.neuroimage.2008.09.036

    • Google Scholar
43. Book

    1. Pavani F
    2. Roder B

    (2012)

    Cross-Modal Plasticity as a Consequence of Sensory Loss: Insights from Blindness and Deafness

    In: Stein BE, editors. The New Handbook of Multisensory Processing. Boston: The MIT Press. pp. 737–759.

    • Google Scholar
44. 1. Philipose LE
    2. Gottesman RF
    3. Newhart M
    4. Kleinman JT
    5. Herskovits EH
    6. Pawlak MA
    7. Marsh EB
    8. Davis C
    9. Heidler-Gary J
    10. Hillis AE

    (2007) Neural regions essential for reading and spelling of words and pseudowords

    Annals of Neurology 62:481–492.

    https://doi.org/10.1002/ana.21182

    • Google Scholar
45. 1. Pleger B
    2. Foerster AF
    3. Ragert P
    4. Dinse HR
    5. Schwenkreis P
    6. Malin JP
    7. Nicolas V
    8. Tegenthoff M

    (2003)

    Functional imaging of perceptual learning in human primary and secondary somatosensory cortex

    Neuron 40:643–653.

    • Google Scholar
46. 1. Porro CA
    2. Francescato MP
    3. Cettolo V
    4. Diamond ME
    5. Baraldi P
    6. Zuiani C
    7. Bazzocchi M
    8. di Prampero PE

    (1996)

    Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study

    The Journal of Neuroscience 16:7688–7698.

    • Google Scholar
47. 1. Power JD
    2. Barnes KA
    3. Snyder AZ
    4. Schlaggar BL
    5. Petersen SE

    (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

    NeuroImage 59:2142–2154.

    https://doi.org/10.1016/j.neuroimage.2011.10.018

    • Google Scholar
48. 1. Powers AR
    2. Hevey MA
    3. Wallace MT

    (2012) Neural correlates of multisensory perceptual learning

    The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 32:6263–6274.

    https://doi.org/10.1523/JNEUROSCI.6138-11.2012

    • Google Scholar
49. 1. Price CJ
    2. Devlin JT

    (2011) The interactive account of ventral occipitotemporal contributions to reading

    Trends in Cognitive Sciences 15:246–253.

    https://doi.org/10.1016/j.tics.2011.04.001

    • Google Scholar
50. 1. Ptito M
    2. Moesgaard SM
    3. Gjedde A
    4. Kupers R

    (2005) Cross-modal plasticity revealed by electrotactile stimulation of the tongue in the congenitally blind

    Brain : A Journal of Neurology 128:606–614.

    https://doi.org/10.1093/brain/awh380

    • Google Scholar
51. 1. Raichle ME
    2. MacLeod AM
    3. Snyder AZ
    4. Powers WJ
    5. Gusnard DA
    6. Shulman GL

    (2001) A default mode of brain function

    Proceedings of the National Academy of Sciences of the United States of America 98:676–682.

    https://doi.org/10.1073/pnas.98.2.676

    • Google Scholar
52. 1. Reich L
    2. Szwed M
    3. Cohen L
    4. Amedi A

    (2011) A ventral visual stream reading center independent of visual experience

    Current Biology : CB 21:363–368.

    https://doi.org/10.1016/j.cub.2011.01.040

    • Google Scholar
53. 1. Rossi S
    2. Hallett M
    3. Rossini PM
    4. Pascual-Leone A
    5. Safety of TMS Consensus Group

    (2009) Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research

    Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology 120:2008–2039.

    https://doi.org/10.1016/j.clinph.2009.08.016

    • Google Scholar
54. 1. Rothlein D
    2. Rapp B

    (2014) The similarity structure of distributed neural responses reveals the multiple representations of letters

    NeuroImage 89:331–344.

    https://doi.org/10.1016/j.neuroimage.2013.11.054

    • Google Scholar
55. 1. Rouder JN
    2. Morey RD

    (2012) Default bayes factors for model selection in regression

    Multivariate Behavioral Research 47:877–903.

    https://doi.org/10.1080/00273171.2012.734737

    • Google Scholar
56. 1. Ruben J
    2. Schwiemann J
    3. Deuchert M
    4. Meyer R
    5. Krause T
    6. Curio G
    7. Villringer K
    8. Kurth R
    9. Villringer A

    (2001) Somatotopic organization of human secondary somatosensory cortex

    Cerebral Cortex 11:463–473.

    https://doi.org/10.1093/cercor/11.5.463

    • Google Scholar
57. 1. Sadato N
    2. Pascual-Leone A
    3. Grafman J
    4. Ibañez V
    5. Deiber MP
    6. Dold G
    7. Hallett M

    (1996) Activation of the primary visual cortex by braille reading in blind subjects

    Nature 380:526–528.

    https://doi.org/10.1038/380526a0

    • Google Scholar
58. 1. Sadato N
    2. Okada T
    3. Honda M
    4. Yonekura Y

    (2002) Critical period for cross-modal plasticity in blind humans: a functional MRI study

    NeuroImage 16:389–400.

    https://doi.org/10.1006/nimg.2002.1111

    • Google Scholar
59. 1. Saito DN
    2. Okada T
    3. Honda M
    4. Yonekura Y
    5. Sadato N

    (2006) Practice makes perfect: the neural substrates of tactile discrimination by mah-jong experts include the primary visual cortex

    BMC Neuroscience 7:.

    https://doi.org/10.1186/1471-2202-7-79

    • Google Scholar
60. 1. Sandrini M
    2. Rossini PM
    3. Miniussi C

    (2008) Lateralized contribution of prefrontal cortex in controlling task-irrelevant information during verbal and spatial working memory tasks: rTMS evidence

    Neuropsychologia 46:2056–2063.

    https://doi.org/10.1016/j.neuropsychologia.2008.02.003

    • Google Scholar
61. 1. Sathian K
    2. Deshpande G
    3. Stilla R

    (2013) Neural changes with tactile learning reflect decision-level reweighting of perceptual readout

    Journal of Neuroscience 33:5387–5398.

    https://doi.org/10.1523/JNEUROSCI.3482-12.2013

    • Google Scholar
62. 1. Satterthwaite TD
    2. Elliott MA
    3. Gerraty RT
    4. Ruparel K
    5. Loughead J
    6. Calkins ME
    7. Eickhoff SB
    8. Hakonarson H
    9. Gur RC
    10. Gur RE
    11. Wolf DH

    (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

    NeuroImage 64:240–256.

    https://doi.org/10.1016/j.neuroimage.2012.08.052

    • Google Scholar
63. 1. Szwed M
    2. Dehaene S
    3. Kleinschmidt A
    4. Eger E
    5. Valabrègue R
    6. Amadon A
    7. Cohen L

    (2011) Specialization for written words over objects in the visual cortex

    NeuroImage 56:330–344.

    https://doi.org/10.1016/j.neuroimage.2011.01.073

    • Google Scholar
64. 1. Szwed M
    2. Qiao E
    3. Jobert A
    4. Dehaene S
    5. Cohen L

    (2014) Effects of literacy in early visual and occipitotemporal areas of chinese and french readers

    Journal of Cognitive Neuroscience 26:459–475.

    https://doi.org/10.1162/jocn_a_00499

    • Google Scholar
65. 1. Urner M
    2. Schwarzkopf DS
    3. Friston K
    4. Rees G

    (2013) Early visual learning induces long-lasting connectivity changes during rest in the human brain

    NeuroImage 77:148–156.

    https://doi.org/10.1016/j.neuroimage.2013.03.050

    • Google Scholar
66. 1. Van Dijk KR
    2. Sabuncu MR
    3. Buckner RL

    (2012) The influence of head motion on intrinsic functional connectivity MRI

    NeuroImage 59:431–438.

    https://doi.org/10.1016/j.neuroimage.2011.07.044

    • Google Scholar
67. 1. Van Essen DC

    (2005) Corticocortical and thalamocortical information flow in the primate visual system

    Progress in Brain Research 149:173–185.

    https://doi.org/10.1016/S0079-6123(05)49013-5

    • Google Scholar
68. 1. Veispak A
    2. Boets B
    3. Ghesquière P

    (2012) Parallel versus sequential processing in print and braille reading

    Research in Developmental Disabilities 33:2153–2163.

    https://doi.org/10.1016/j.ridd.2012.06.012

    • Google Scholar
69. 1. Veispak A
    2. Boets B
    3. Ghesquière P

    (2013) Differential cognitive and perceptual correlates of print reading versus braille reading

    Research in Developmental Disabilities 34:372–385.

    https://doi.org/10.1016/j.ridd.2012.08.012

    • Google Scholar
70. 1. Vogel AC
    2. Petersen SE
    3. Schlaggar BL

    (2014) The VWFA: it’s not just for words anymore

    Front. Hum. Neurosci 8:.

    https://doi.org/10.3389/fnhum.2014.00088

    • Google Scholar
71. 1. Voss MW
    2. Prakash RS
    3. Erickson KI
    4. Boot WR
    5. Basak C
    6. Neider MB
    7. Simons DJ
    8. Fabiani M
    9. Gratton G
    10. Kramer AF

    (2012) Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks

    NeuroImage 59:138–148.

    https://doi.org/10.1016/j.neuroimage.2011.03.052

    • Google Scholar
72. 1. Weissenbacher A
    2. Kasess C
    3. Gerstl F
    4. Lanzenberger R
    5. Moser E
    6. Windischberger C

    (2009) Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies

    NeuroImage 47:1408–1416.

    https://doi.org/10.1016/j.neuroimage.2009.05.005

    • Google Scholar
73. 1. Wilson M

    (1988) MRC psycholinguistic database: machine-usable dictionary, version 2.00

    Behavior Research Methods, Instruments, & Computers 20:6–10.

    https://doi.org/10.3758/BF03202594

    • Google Scholar
74. 1. Wright ND
    2. Mechelli A
    3. Noppeney U
    4. Veltman DJ
    5. Rombouts SARB
    6. Glensman J
    7. Haynes J-D
    8. Price CJ

    (2008) Selective activation around the left occipito-temporal sulcus for words relative to pictures: individual variability or false positives?

    Human Brain Mapping 29:986–1000.

    https://doi.org/10.1002/hbm.20443

    • Google Scholar
75. 1. Xia M
    2. Wang J
    3. He Y

    (2013) BrainNet viewer: a network visualization tool for human brain connectomics

    PloS One 8:e68910.

    https://doi.org/10.1371/journal.pone.0068910

    • Google Scholar
76. 1. Xue G
    2. Chen C
    3. Jin Z
    4. Dong Q

    (2006) Language experience shapes fusiform activation when processing a logographic artificial language: an fMRI training study

    NeuroImage 31:1315–1326.

    https://doi.org/10.1016/j.neuroimage.2005.11.055

    • Google Scholar
77. 1. Yan CG
    2. Cheung B
    3. Kelly C
    4. Colcombe S
    5. Craddock RC
    6. Di Martino A
    7. Li Q
    8. Zuo XN
    9. Castellanos FX
    10. Milham MP

    (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

    NeuroImage 76:183–201.

    https://doi.org/10.1016/j.neuroimage.2013.03.004

    • Google Scholar
78. 1. Yarkoni T
    2. Balota D
    3. Yap M

    (2008) Moving beyond coltheart's n: a new measure of orthographic similarity

    Psychonomic Bulletin & Review 15:971–979.

    https://doi.org/10.3758/PBR.15.5.971

    • Google Scholar
79. 1. Zangaladze A
    2. Epstein CM
    3. Grafton ST
    4. Sathian K

    (1999) Involvement of visual cortex in tactile discrimination of orientation

    Nature 401:587–590.

    https://doi.org/10.1038/44139

    • Google Scholar
80. 1. Zangenehpour S
    2. Zatorre RJ

    (2010) Crossmodal recruitment of primary visual cortex following brief exposure to bimodal audiovisual stimuli

    Neuropsychologia 48:591–600.

    https://doi.org/10.1016/j.neuropsychologia.2009.10.022

    • Google Scholar

Author details

1. Katarzyna Siuda-Krzywicka

   1. Department of Psychology, Jagiellonian University, Kraków, Poland
   2. INSERM U 1127, CNRS UMR 7225, Sorbonne Universités, and Université Pierre et Marie Curie-Paris 6, UMR S 1127, Institut du Cerveau et de la Moelle épinière (ICM), Paris, France

   Contribution

   KSK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Łukasz Bola

   Competing interests

   The authors declare that no competing interests exist.

2. Łukasz Bola

   1. Department of Psychology, Jagiellonian University, Kraków, Poland
   2. Laboratory of Brain Imaging, Neurobiology Center, Nencki Institute of Experimental Biology, Warsaw, Poland

   Contribution

   ŁB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Katarzyna Siuda-Krzywicka

   Competing interests

   The authors declare that no competing interests exist.

3. Małgorzata Paplińska

   Academy of Special Education in Warsaw, Warsaw, Poland

   Contribution

   MP, Designed and taught the Braille course

   Competing interests

   The authors declare that no competing interests exist.

4. Ewa Sumera

   Institute for the Blind and Partially Sighted Children in Krakow, Kraków, Poland

   Contribution

   ES, Designed and taught the Braille course

   Competing interests

   The authors declare that no competing interests exist.

5. Katarzyna Jednoróg

   Laboratory of Psychophysiology, Department of Neurophysiology, Nencki Institute of Experimental Biology, Warsaw, Poland

   Contribution

   KJ, Additional contributions to acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

6. Artur Marchewka

   Laboratory of Brain Imaging, Neurobiology Center, Nencki Institute of Experimental Biology, Warsaw, Poland

   Contribution

   AM, Additional contributions to acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

7. Magdalena W Śliwińska

   Department of Experimental Psychology, University College London, London, United Kingdom

   Contribution

   MWŚ, Additional contributions to acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

8. Amir Amedi

   1. The Cognitive Science Program, The Hebrew University of Jerusalem, Jerusalem, Israel
   2. Department of Medical Neurobiology, The Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
   3. The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
   4. Sorbonne Universite´s, UPMC Univ Paris 06, Institut de la Vision, Paris, France

   Contribution

   AA, Additional contributions to conception and design, data analysis, and revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Marcin Szwed

   Department of Psychology, Jagiellonian University, Kraków, Poland

   Contribution

   MS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   mfszwed@gmail.com

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-2153-7793

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