Recycling Neurons for Reading

Accesibility: Intermediate-Advanced

Our brains have evolved to be good at certain things: seeing, hearing, learning language, and interacting with other similar brains, to name a few examples. But say you want it to do something new – look at symbols on a page and map them to language. In other words, you want to teach your brain to read. How would you go about doing this? What parts of the brain would you use?

Unless you plan on developing a completely new region, it makes sense to repurpose the brain regions you already have -- a process that neuroscientist Stanislas Dehaene refers to as “neuronal recycling.” This raises the question -- what regions are recycled? And do the regions that get co-opted become worse at their original function?



Dehaene and colleagues explored this question by scanning adults at different levels of literacy: literates, ex-literates (adults who used to be illiterate but learned to read in adulthood), and illiterate adults. They had several interesting findings:

1. They first looked at whether learning to read changes brain activation when looking at words. Not surprisingly, it does. Reading performance was correlated with increased brain activation in much of the left hemisphere language network, including the visual word form area. And this increased activation appeared to be specific to word-like stimuli.

2. During reading, ex-literates have more bilateral activation and also recruited more posterior brain regions. This is similar to what we find in children, who also show more spread out activation while reading. This suggests that unskilled readers recruit a wider set of brain regions as they are learning to read. As readers become more skilled, their brains become more efficient and recruit fewer regions

3. In literate adults, response to checker boards and faces in the visual word form area was lower in the visual word form area compared to non-readers. This suggests that learning to process words may actually be taking resources away from processing other stimuli.

4. The researchers looked more closely at responses to other faces and houses to see how exactly learning to read competed with other visual functions. They found that activation in the peak voxels for faces and houses did not change with literacy. However, activation in surrounding voxels did decrease.

5. And here's an interesting result. Since reading is a horizontal process (at least in the languages they were testing), the researchers checked to see if the visual system became more attuned to horizontal stimuli. They found that literacy enhanced response to horizontal but not vertical checker boards in some primary visual areas.


Dehaene S, Pegado F, Braga LW, Ventura P, Nunes Filho G, Jobert A, Dehaene-Lambertz G, Kolinsky R, Morais J, & Cohen L (2010). How learning to read changes the cortical networks for vision and language. Science (New York, N.Y.), 330 (6009), 1359-64 PMID: 21071632

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Evidence Suggesting that Specialized Visual Regions Are Formed by Pruning in Early Childhood

There are quite a few specialized visual regions in the brain. For example, the fusiform face area (FFA) activates for faces, and the visual word form area (VWFA) in the left fusiform is consistently active for words.

How do these specialized cortical regions develop? Is it experience dependent? Do regions have a preexisting preference for certain visual features? (For example, perhaps the visual word form region prefers high contrast stimuli with sharp borders). Do these regions form by increasing activation to preferred stimuli, or a decreasing activation to nonpreferred stimuli? Cantlon and colleagues investigated these questions in a recent study.



They tested prereading five year olds and adults in an fMRI experiment. Participants saw faces, letters, numbers, shoes and scrambled images and pressed a button if a green border appeared around the picture. There were two interesting findings.

The first concerned the visual word form area. Both adults and children had a specialized brain region in the left fusiform that activated more for letters than objects. However, while adults activated that region more for letters than for numbers, children had equally high activation for letters and numbers.*

These results support a role for both experience and low level visual features in the development of the visual word form area. Note that these children are nonreaders, but they already activate the left fusiform for letters and numbers. So perhaps there’s something hardwired in the left fusiform that prefers symbol-like, high contrast, visual stimuli. But only adults, who have had extensive experience with letters, show differential activation for words and numbers.

The authors then investigated the relationship between activation level and behavior. They tested children on a face matching task and a letter naming task. Contrary to what you might expect, activation in the fusiform face area did not correlate with face matching skill, and activation in the visual word form area did not correlate with letter naming skill.

Rather, skill was negatively correlated with activation to the nonpreferred category. Face matching performance was inversely correlated with FFA activation to shoes. And letter naming was inversely correlated with VWFA activation to faces. This suggests that that increased skill in face and letter recognition is associated not with enhancing activation to preferred stimuli, but with pruning back activation to unrelated stimuli. **

*Methodological note: ROI selection, 10 strongest voxels within a sphere 10mm radius around peaks of All>scrambled.

**Note that not all nonpreferred stimuli show this inverse correlation. In the face area, there was no correlation between face skill and symbols, and in the VWFA, there is no correlation between letter naming skill and shoe activation. Perhaps these nonpreferred stimuli too far from the preferred stimulus, so no pruning is needed?



Cantlon JF, Pinel P, Dehaene S, & Pelphrey KA (2010). Cortical Representations of Symbols, Objects, and Faces Are Pruned Back during Early Childhood. Cerebral cortex (New York, N.Y. : 1991) PMID: 20457691

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Developmental Changes in Word Processing After Adolescence

When does brain development for reading stop? We often focus on school aged children, but what about the later teen years? To answer this question, Brem and colleagues tested adolescents (age 15-17) and adults (19-31) in a study using fMRI and EEG.



Participants were presented with words and symbols strings and asked to detect repeats. It’s an easy task, so it’s not surprising that the two groups had equal reading accuracy and speed. However, there were brain differences.

Brem focused on two early ERP components. The P1 component, a positive peak at 100 ms, is sensitive to low level stimulus characteristics like luminance and size. Brem found that this component had a higher amplitude for symbol strings and for words in both groups.

The N1 component occurs later (140-220ms) and is sensitive to higher level factors like stimulus category. Brem found that the later part of the N1 component was more pronounced to words than symbol strings. Source localization on the N1 component  found that the early part of the N1 localized to the temporal parietal occipital junction, while the late N1 localized to the left fusiform.

There were differences between the two groups. Adolescents had higher P1 and N1 amplitudes than adults. The N1 latency also became faster with age for words but not symbol strings.

Brem also used fMRI to look at the spatial organization of the fusiform gyrus*. Posterior fusiform regions responded more to symbol strings than words, while anterior regions responded more to words than symbol strings.

The left fusiform region seems to be related to reading skill. Bigger N1 amplitude was correlated with fewer mistakes in a reading test.  Higher fMRI signal in the anterior fusiform was correlated with faster reading.

It’s interesting that despite similar behavior between groups, brain measures still differ. I do wonder about differences within the adults as well. 19-31 is a pretty big range, so I'd like to see what happens after age 18.

*Using five regions of interest. 6 mm spheres based on Taleraich coordinates.


Brem S, Bucher K, Halder P, Summers P, Dietrich T, Martin E, & Brandeis D (2006). Evidence for developmental changes in the visual word processing network beyond adolescence. NeuroImage, 29 (3), 822-37 PMID: 16257546

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Posterior Brain Differences in Children with Dyslexia

Accessibility:  Intermediate-Advanced

I realized after the last post that we haven’t actually spent much time discussing brain differences between dyslexic and nonimpaired readers. So today, I’m covering an earlier experiment by the Shaywitz’s.



In a 2002 paper, Shaywitz and colleagues reported an experiment with 144 children aged 7-17, half dyslexic and half nonimpaired. The children performed several tasks in the scanner, but the paper focuses on two: nonword rhyme (NWR) (Does [PEAT] rhyme with [LEAT]?) and semantic categorization (CAT)(Are [CORN] and [RICE] in the same category?). A line match task was used as a baseline.

During fMRI, the nonimpaired readers showed more activation than dyslexic readers in a large number of left and right hemisphere brain regions.*

The authors also looked at brain regions where reading skill correlated with activation. The left occipitotemporal (OT) region correlated with skill in both tasks, while bilateral parietotemporal regions showed a correlation with skill in the categorization task only.

This isn’t the first time activation in the OT has been linked to reading skill. Specht 2009 found that OT activation during a categorization task correlates with reading score even before formal reading instruction. Shaywitz 2004 found activation increases in the left OT region a year after completion of a phonological intervention. Also, this paper reported negative correlation between reading skill and activation in right OT gyrus during a categorization task, a correlation that was also reported in Turkeltaub 2003**.

Finally, the authors looked at brain regions where activation correlates with age and found striking differences between dyslexic and nonimpaired readers. Dyslexic readers had many regions that increased in activation with age***. In normal readers, there were few correlations with increasing age, and age correlated negatively with superior frontal and middle frontal regions. One possible explanation is that dyslexics learn to compensate with other brain regions as they grow older. The normal readers, on the other hand, get more efficient in their reading.****

The age result also highlights the variability in the sample. Children with dyslexia change greatly in brain activation as they grow older. You can imagine the variability that this would produce in an random experimental sample of 15 kids. I wonder if there’s been much work on relative variability in dyslexic children vs. nonimpaired children.

*NWR in left hemisphere sites (inferior frontal gyrus, superior temporal sulcus, superior temporal gyrus, middle temporal gyrus, middle occipital gyrus) and right hemisphere (Inferior frontal, superior temporal sulcus, middle temporal gyrus, medial orbital.) CAT in left (angular gyrus, middle temporal gyrus, middle occipital) and in right (middle temporal gyrus, middle occipital)

**Turkeltaub didn’t find a positive correlation in left OT, and also used a lower level task (tall letter detection.

*** IN NWR in DYS, increased age correlated with activation in bilateral IFG, basal ganglia, posterior cingulate, cuneus, middle occipital gyri and left STG.

****Correlations with age in dyslexics and normal readers are also explored in Shaywitz 2007. In that paper, they do report regions in nonimpaired readers that increase activation with age. It might be the same dataset, but I’m not sure.



Shaywitz, B. (2002). Disruption of posterior brain systems for reading in children with developmental dyslexia Biological Psychiatry, 52 (2), 101-110 DOI: 10.1016/S0006-3223(02)01365-3

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The Development of Visual Word Recognition

Accessibility:  Intermediate-Advanced

We’ve looked at brain regions and development during word related tasks (word generation, reading and repeating), but we haven’t yet looked at a straight up study of word recognition and development.



What’s the best task to use to study visual word recognition? You can have people read out loud, but that involves processes like speech generation. Likewise, reading sentences or paragraphs requires the reader to process meaning and grammar in addition to the words on the page.

One segment of the field has gravitated towards tasks of single word processing that don’t require reading at all. In this particular study, Turkeltaub and colleagues use a tall letter detection task. The subjects press a button if the word has a tall letter (like d or l). As a control condition, subjects perform the same task on false fonts. Even though you can do this task without reading the words, the assumption is that reading, being highly automatic, will occur anyways. This approach, focusing on the automatic, bottom up process, allows for a more tightly controlled study. However, it also limits the findings to that very thin slice of the reading process.

Turkeltaub and colleagues tested forty one subjects ranging from 8 to 20 years old. In the whole group, the words > symbols contrast gives activation in the left posterior temporal, left inferior frontal, and right inferior parietal regions.

The authors also looked at correlations between activation and reading ability. The trend here seems to be increasing lateralization (more reliance on left hemisphere regions and less reliance on right hemisphere regions), with reading skill.*   Interesting.  I wonder how this relates to lateralization of spoken language.

Finally, the authors looked for regions that correlated with other behavioral measures, including phonetic working memory (left intraparietal sulcus and left and right middle frontal gyri), phonological awareness (left hemisphere network, incluing posterior STS and ventral inferior frontal), and phonological naming (bilateral network, including right posterior superior temporal, right middle tempral, and left ventral inferior frontal.) Surprisingly (to me at least) there is almost no overlap between the regions for the three measures. This could either mean that these measures involve very different cognitive and neural processes, or that the automatic task used in this experiment was not suited for accurately tapping into these abilities.

*Reading ability correlated positively with activation left hemisphere frontal and temporal cortical areas, and negatively with right hemisphere posterior regions. There was no correlation in the left fusiform (visual word form area), but there is a negative correlation in right posterior fusiform. 

Turkeltaub, P., Gareau, L., Flowers, D., Zeffiro, T., & Eden, G. (2003). Development of neural mechanisms for reading Nature Neuroscience, 6 (7), 767-773 DOI: 10.1038/nn1065

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Dyslexia Brain Differences Show Up Before Formal Reading Instruction

Last time, we talked about early behavioral differences between prereading children that predicted future reading impairment. Today, we’re continuing on the theme of early predictive differences, this time in the brain.



The question of how early brain differences arise is a worthwhile one. We want to know whether the dyslexic brain is tackling reading differently from the very beginning or if these brain differences arise after some reading experience, perhaps reflecting compensatory strategies that the children may have developed.

Specht and colleagues (Scandinavian Journal of Psychology 2008) conducted a brain imaging study on Norwegian children (a good population to study because reading instruction starts in second grade in Norway). The basic goal of their experiment was to scan 6 year olds (before they learned to read) and see if they process words differently depending on their risk for dyslexia. Unlike the Lervag study, this study was not longitudinal. Specht and colleagues determined which kids were at risk for dyslexia using a risk index that took into account factors like heredity, language development, and other factors.

Kids looked at four kinds of stimuli during an fmri scan: pictures, logos, regular words and irregular words while performing a categorization task (“Is this something you can play with?” and similar questions). I won’t spend too much time comparing between conditions because I’m not clear on what characteristics were controlled for between the stimulus types.

There were differences between the at-risk and normal reading group in all conditions. There were several interesting findings. First, risk index score correlated with increased activation when looking at words in the angular gyrus, an area that has been reported to be involved in language/phonological processing.

Our old friend, the visual word form area, also shows up. At a more liberal statistical threshold ( p<.001 uncorrected and with a small volume correction) they found that risk index score correlates negatively with left occipitotemporal activation when viewing irregular words.

So what does this mean? For one thing, differences arise early, before formal reading instruction. The two groups did not differ significantly on standardized reading measures at the time of testing (although there was a trend (p<.096) towards a difference in reading scores. So there seems to be something different about how these kids approach words from the very beginning. It would be interesting to know what is driving these differences. I wonder what strategies the kids were using to in the scanner to complete the categorization task, especially since they couldn’t read yet. For word conditions, kids only had an accuracy of 20-30%. What were they doing for the words they couldn’t read? Were the scanner differences driven by the words they could recognize, or all the words?

I’m particularly puzzled by the VWFA findings. The VWFA is usually thought to develop based on expertise with letters Does this mean that even before reading instruction there is some difference in expertise between kids at risk and not at risk? Interesting questions for future investigation.

Specht K, Hugdahl K, Ofte S, Nygård M, Bjørnerud A, Plante E, & Helland T (2009). Brain activation on pre-reading tasks reveals at-risk status for dyslexia in 6-year-old children. Scandinavian journal of psychology, 50 (1), 79-91 PMID: 18826418

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