N1 Specialization in Children with Dyslexia

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It's been a little while, but we've been talking about the N1 component and how it relates to reading. Just to recap, the N1 component is an ERP component occurring at around 170 ms. In normal reading adults, the component is stronger for words than for symbols. We will refer to the words minus symbols difference as “N1 specialization for words .” Pre-reading kindergartners do not have this N1 specialization, while second graders have a stronger N1 specialization compared to adults. Today we focus on children with dyslexia.
As you might've guessed, Maurer and colleagues did the same experiment on children with dyslexia as well (see previous article for more information on what they did). These are the findings:

1. N1 specialization for words over symbols was much reduced in dyslexic second graders compared to normal reading second graders.

2. N1 specialization correlated with reading speed in second graders.

3. Interestingly, although dyslexic second graders had reduced specialization, they actually had a greater specialization two years earlier (in kindergarten) than their normal reading counterparts. I'm not quite sure why this would be.

4. In addition to the N1 difference, there was also a reduced response in the earlier P1 component in children with dyslexia (at both ages – kindergarten and second grade). This reduction was general for both words and symbols though, and not specialized to words.




Maurer U, Brem S, Bucher K, Kranz F, Benz R, Steinhausen HC, & Brandeis D (2007). Impaired tuning of a fast occipito-temporal response for print in dyslexic children learning to read. Brain : a journal of neurology, 130 (Pt 12), 3200-10 PMID: 17728359

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Brain Measures Predict Future Improvement in Children With Dyslexia

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Disclaimer: My PI is an author on this paper.

There is a lot of variability in outcomes for children diagnosed with dyslexia. Some children improve greatly over time, while others don't. Today, we're looking at a paper that asks whether it's possible to predict improvement in children with dyslexia.

Fumiko Hoeft and colleagues scanned children with and without dyslexia while performing a word rhyme task. They also tested the children on several reading measures. Two and half years later, they retested the children again on the same reading measures. Some of the children improved, while others didn't . The question then, is whether there is something from the brain scans or test scores in the first session that can predict performance 2 1/2 years later.



The researchers found two brain measures that predicted improvement in reading skills: greater white matter integrity in the right superior longitudinal fasciculus, and activation in the right inferior frontal gyrus during the rhyming task. Note that these regions are not your typical language regions. In fact, they are the right hemisphere counterparts of language processing regions in typical readers. Also, these didn’t correlate with reading improvement in control readers.This suggests that rather than imitating what typical readers are doing, the dyslexics who improve are bringing in compensatory mechanisms.

So if we have a dyslexic child, how accurately can we predict future improvement? The researchers found that brain data from those two regions by themselves predicted reading gains with 72% accuracy. When the researchers used data from the entire brain, they predicted reading gains with 90% accuracy. (Chance would be 50%. The researchers were trying to predict whether a child’s improvement was below or above the median improvement for the entire group.)

These results are an interesting case of brain data giving us more information the behavioral measures. None of the behavioral measures predicted which children would improve, but the brain data did.

One might ask how useful these results would be for dyslexics. On the one hand, any information is helpful. On the other, if you are in the group predicted to not show improvements, would you really want to know? One good thing about this type of research is that perhaps if we keep going in this direction, we might be able to not only predict improvement, but predict improvement to different types of interventions, thus leading to better treatment.


Hoeft F, McCandliss BD, Black JM, Gantman A, Zakerani N, Hulme C, Lyytinen H, Whitfield-Gabrieli S, Glover GH, Reiss AL, & Gabrieli JD (2011). Neural systems predicting long-term outcome in dyslexia. Proceedings of the National Academy of Sciences of the United States of America, 108 (1), 361-6 PMID: 21173250

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White Matter and Reading Ability

Accessibility:  Intermediate-Advanced

Hello folks.  Things are pretty busy over here and I might be having to review a lot of papers soon, so there's a possibility that entries here will get shorter and a bit more technical.  But we'll see.

Since reading is by nature a multimodal task involving both visual and language regions, it makes sense to look at brain connections in dyslexia. I've written once about white matter in dyslexia, when I blogged Bernard Chang’s PNH study. Today I'll cover two other studies that look at white matter and reading.



As a quick recap, brain tissue is often categorized into gray and white matter. White matter consists mostly of axons, the parts of neurons that send signals to other neurons. Therefore, white matter tracts carry information between brain regions and diffusion tensor imaging is a technique often used to study white matter.  You can take several measures with DTI, but one common one is fractional anisotropy, a measure of the directionality of water diffusion.  You can think of it as a measure of white matter integrity.

In one study, James Andrews and colleagues measured white matter integrity in preterm*  and term children. They found a correlation between reading skill and fractional anisotropy  in the corpus callosum, the large white matter tract that connects the two hemispheres. They also found a trend toward a correlation between reading skill and fractional anisotropy in the left temporal parietal region, a region often associated with reading. I'm surprised by the corpus callosum finding, and wonder its role might be in reading. Is the corpus callosum connecting language regions to their right hemisphere homologues? I also wonder if this is something general to the population, or a difference unique to preterm children. I guess we’ll have to see if this finding comes up in later studies.

Another DTI study found some more predictable results. Rimrodt and colleagues scanned the brains of children with dyslexia and normal-reading children between the ages of seven and 16 years. They found that children with dyslexia had lower FA in the left inferior frontal gyrus and the left temporoparietal region, both areas previously implicated in reading. Interestingly, they also found that the FA in some posterior areas involved in visual word processing (including the left fusiform) were correlated with speeded word reading.

*mean gestational age 30.5 weeks

ANDREWS, J., BEN-SHACHAR, M., YEATMAN, J., FLOM, L., LUNA, B., & FELDMAN, H. (2009). Reading performance correlates with white-matter properties in preterm and term children Developmental Medicine & Child Neurology, 52 (6) DOI: 10.1111/j.1469-8749.2009.03456.x

Rimrodt, S., Peterson, D., Denckla, M., Kaufmann, W., & Cutting, L. (2010). White matter microstructural differences linked to left perisylvian language network in children with dyslexia Cortex, 46 (6), 739-749 DOI: 10.1016/j.cortex.2009.07.008

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Noise Exclusion Deficits in Dyslexia

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The human visual system includes two pathways, magnocellular and parvocellular, deriving from two types of retinal ganglion cells that project to different layers of the lateral geniculate nucleus. Generally speaking, the magnocelluar pathway is specialized for movement while the parvocellular pathway is specialized for color and detail.  Some researchers have found dyslexia to be associated with magnocelluar impairment, although evidence has been mixed.

A paper from Sperling and colleagues argues that magnocelluar deficits in dyslexica may actually be a deficit in noise exclusion.  The authors tested children with and without dyslexia using stimuli that were designed to activate the magnocellular or parvocellular pathways. The magnocellular stimulus was a patch with white bars that alternated rapidly between light and dark. The parvocellular stimulus had thin light and dark bars that did not alternate.



In addition to the two stimulus types, there was a high noise and low noise condition. In the low noise condition, one of the stimuli appeared to the left or right of the fixation mark. In the high noise condition, noise patches appeared on either side of fixation and the stimulus was overlaid onto one of the noise patches. In both cases, child had to say on which side the stimulus appeared.

The authors calculated contrast thresholds (the amount of contrast needed between the light and dark bars for accurate detection) for both groups of children. They found no difference in the contrast thresholds for the low noise condition. In the high noise condition, dyslexic children had higher contrast thresholds (more difficulty detecting) for both the magnocellular and parvocellular stimuli. In addition, thresholds in the high noise condition were correlated with language measures.

These are interesting results. While one study cannot rule out the magnocellular theory of dyslexia, this does open the possibility that many of the results that pointed to a magnocellular deficit were actually cases of noise exclusion deficit.   I do remember one paper about motion perception and dyslexia that can't be explained by noise, so I'll see if I can write about that later.

Another question is, how does noise exclusion lead to dyslexia? It could be that a noise exclusion deficit results in difficulties building phonological categories, which in turn affect reading. The authors also mention that noise exclusion could affect learning in the visual modality by making it harder to extract regularities from different fonts and scripts.


Sperling, A., Lu, Z., Manis, F., & Seidenberg, M. (2005). Deficits in perceptual noise exclusion in developmental dyslexia Nature Neuroscience DOI: 10.1038/nn1474

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Sensitivity and Specialization in the Occipitatemporal Region: Differences in Dyslexic Children

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Early research on the role of the occipitotemporal region in reading often focused on characterizing a single region in the mid fusiform, commonly called the visual word form area. Since then, focus has gradually shifted from a single region to the entire length of the occipitotemporal region, looking at how the sensitivity and tuning changes as you move from posterior to anterior regions.



Van der mark used an approach like this to look at dyslexic and control children aged 9-12 years. Eighteen normal reading and twenty four dyslexic children performed a phonological lexical decision task in the scanner. Children saw words, pseudohomophones (words that sounded like real words but spelled differently, like “taksi”), pseudowords (pronounceable nonwords), and false fonts. The children were asked to decide whether something sounded like a real word. For example, the correct response would be “yes” for words and pseudohomophones and “no” for pseudowords and false fonts.

The children with dyslexia did worse for pseudohomophones and pseudowords and performed similarly to the controls for words and false fonts.

The authors report two main findings. First, the control children showed a gradient of print specialization in the occipitotemporal region, with more activation to false fonts in posterior regions and more activation to real letters and anterior regions. The control children did not show this trend.

Second, control showed more activation for pseudowords and pseudohomophones than words, while children with dyslexia didn't.

This is a nice study that takes a more nuanced approach to dyslexia brain differences. Brem and colleagues also got similar results with the words and false fonts.

By now there's quite a bit of literature on the specialization of the visual word form area. My own struggle, as I’m also doing this type of research, is the question of what does it all mean? We have all the studies now showing brain differences between control and dyslexic children, but what does it mean to have more or less activation? That the brains of dyslexic children process words differently? I could've told you that before we stared.

So what would help? Perhaps the next step in dyslexia research, now that we've mapped out the basic differences, is to zoom in as much as we can on the relationships between brain differences and behavioral differences. Perhaps more fine grained behavioral measures would help, or more interventional studies that looked at brain activation before and after training. It may also help to look at functional connectivity and how different brain regions interact. Anyone else have ideas?



van der Mark S, Bucher K, Maurer U, Schulz E, Brem S, Buckelmüller J, Kronbichler M, Loenneker T, Klaver P, Martin E, & Brandeis D (2009). Children with dyslexia lack multiple specializations along the visual word-form (VWF) system. NeuroImage, 47 (4), 1940-9 PMID: 19446640

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A Meta-Analysis of Dyslexia Brain Imaging Studies

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fMRI experiments, with their small sample sizes, can easily fall victim to variability within the subject pool. This is especially true for patient studies. So it’s nice to step back and look at the big picture once in a while, and see where different studies agree and disagree.



Richlan and colleagues recently did meta-analysis of dyslexia brain imaging studies. They used an algorithm called Activation Likelihood Estimation (ALE), which models foci of activation as Gaussian probability distributions. (The software is called GingleALE. Ha.)

Richlan and colleagues picked studies  with the following criteria:
1) Uses words, pseudowords or single letters as stimuli
2) Uses reading or reading related task in the scanner, and
3) Group comparisons are done in a standard stereotactic space.
The studies included PET and fMRI studies.

The take away message is that people with dyslexia underactivate posterior reading regions and may overactivate  frontal regions.

The authors found underactivation in regions associated with the phonological reading pathway (reading by sounding out words), including the superior temporal gyrus and inferior parietal lobule. Interestingly, they found no difference in the angular gyrus, a region that has often been reported to be important to reading.

They also found underactivation the pathway associated with automatic whole word reading, including the left fusiform, inferior temporal and middle temporal regions.

At a less conservative threshold, the authors found that people with dyslexia overactivated the left inferor frontal region. This is typically interpreted as frontal regions being brought in to compensate for posterior reading regions.

They did find one posterior region as well that was overactivated in people with dyslexia : the left lingual gyrus, a lower level visual region. Perhaps again, a case of compensation.

All in all, a nice summary of dyslexia results. Again, I wonder about the relative variablility of dyslexics and controls, and how they affected the results.


Richlan, F., Kronbichler, M., & Wimmer, H. (2009). Functional abnormalities in the dyslexic brain: A quantitative meta-analysis of neuroimaging studies Human Brain Mapping, 30 (10), 3299-3308 DOI: 10.1002/hbm.20752

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Posterior Brain Differences in Children with Dyslexia

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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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Phonological Training Changes Brain Activation in Dyslexic Children

Note: Online Universities has included me in their list of top 50 female science bloggers. It’s not actually for this blog, but for my Brain Science and Creative Writing blog. Anyways, check out the list if you get a chance. There are lot of interesting bloggers.

 Accessibility:  Intermediate-Advanced

We’ve looked at the neuroscience of dyslexia and how the dyslexic brain processes words. Our ultimate goal, however, is treatment. Therefore, we’d like to see whether reading interventions cause brain changes in reading-impaired children. In a 2004 paper in Biological Psychiatry, Shaywitz and colleagues investigated this question.



The study focused on kids aged 6-9, divided into three groups. The experimental group consisted of reading-disabled students who went through an eight month experimental intervention that focused on  phonology: letter-sound associations, combining sounds, etc.. Another group of reading-impaired children were put in community intervention control group that participated in a variety of reading interventions, including remedial reading and tutoring. However, there was no specific focus on phonology. A third group, community control, consisted of normal reading children.*

All groups improved in their reading measures after 8 months (not surprising, since they continued to attend school). The experimental group showed more improvement than the community intervention group in one reading measure.

Shaywitz and colleagues were interested in brain differences before and after intervention. They scanned the kids pre/post intervention in a letter identification task.** Their main analysis was a second order comparion. They first determined the pre/post intervention changes within each group. Then they compared the changes between groups.

Compared to the community intervention control, both the experimental intervention and normal-reading control group showed a greater increase in left inferior frontal gyrus (often involved in phonological processing) activation. The experimental intervention group showed more increase in left middle temporal gyrus activation compared to the community intervention group.

In addition to comparing pre/post intervention differences between groups, Shaywitz also scanned the experimental group a year after finishing the intervention. The group showed continued increases in several left hemisphere areas, including left inferior frontal, superior temporal, and left occipitotemporal regions***. Also, they showed a decrease in right MTG and right caudate activation. This falls in line with the increase in left lateralization we saw in Turkeltaub 2003.

What’s the take home message? This study shows us that phonological intervention results in measurable brain changes in the left inferior frontal gyrus, a phonological region. This is encouraging. However, how does this actually impact reading performance? The experimental group only performed significantly better than the community intervention group in one reading measure, although it looks like they performed slightly better (but not statistically significant) in other measures. So there is a hint that phonological interventions might be more valuable than other interventions, but we’d have to get more data on this.

The study also shows that brain regions in the experimental group continue to develop during the year after the intervention. When did these changes start – during intervention or afterwards? It's hard to tell because they don't report the changes in the experimental intervention group right after intervention. They only report on the difference in changes between groups.

Also are these changes jumpstarted by the intervention, or would they have occurred anyway? Unfortunately, we can’t answer that question either. While the authors had hoped to also scan the two other groups a year afterwards, they were unable to.

Anyways, it's kinda cool to see brain differences as a result of training. It will be interesting to see in future studies what is going on in more detail.

*Children from the EI group were from Syracuse, NY, while the other two groups were recruited from New Haven.
**The kids heard a letter name and had to choose the correct letter from two options. This task was compared against baseline of hearing tone and specifying position of asterisk.
***LIFG, STG, left OT, left lingual, and left inferior occipital


Shaywitz BA, Shaywitz SE, Blachman BA, Pugh KR, Fulbright RK, Skudlarski P, Mencl WE, Constable RT, Holahan JM, Marchione KE, Fletcher JM, Lyon GR, & Gore JC (2004). Development of left occipitotemporal systems for skilled reading in children after a phonologically- based intervention. Biological psychiatry, 55 (9), 926-33 PMID: 15110736

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Rats Who Can't Read Good: A Rodent Model for Dyslexia

Accesibility:  Intermediate

Dyslexic rats? Really? Well, these rats can’t read, but they’re still used as an animal model for dyslexia.



First, some background. The underlying cause of dyslexia is still under debate, but it’s generally accepted that it involves deficits in auditory and phonological (language sounds) processing, with a possibility of visual deficits as well. Post mortem studies of dyslexic human brains have turned up brain anomalies, including cortical ectopias (nests of neurons in the wrong layer in the cortex) and focal microgyri (micro folding). Researchers have also found abnormalities in the thalamus and cerebellum.

Dyslexia rat models are created by inducing these same abnormalities, usually focal microgyria and molecular layer ectopias, in rats. Interestingly, some of these rats develop deficits in rapid auditory processing, which is important for phonological processing in humans. Introducing microgyria also causes thalamic changes in male rats, similar to dyslexic thalami in humans. The thalamic changes are also associated with auditory perceptual deficits in the males.

Another interesting observation:  boys are more at risk than girls for dyslexia, and the same trend occurs in rats. Young male rats have a higher risk for developing rapid auditory processing deficits from induced cortical malformations. There seems to be something about the male brain that increases risk for language related disorders.

Hmm, anyone want to start Hooked on Phonics for rodents?

[And kudos to anyone who got the Zoolander reference in the title]

Galaburda AM, LoTurco J, Ramus F, Fitch RH, & Rosen GD (2006). From genes to behavior in developmental dyslexia. Nature neuroscience, 9 (10), 1213-7 PMID: 17001339

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Dyslexic vs. Nonimpaired Readers: Differences in Brain Development

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Studies comparing normal reading and dyslexic children often take a snapshot approach, comparing brain function at specific ages. However, these studies don’t tell us how these differences fit into the developmental picture. Are dyslexics following the same developmental course as normal readers, just at a different rate? Or do dyslexic brains develop in a completely different way?



Instead of comparing activation at each age, Shaywitz and colleagues compared the way the two groups changed throughout development. They conducted a massive imaging study involving 113 dyslexic children (ages 7-18) and 119 nonimpaired children aged (7-17). The participants did two tasks: a line match task (Do ///\ and //// match?) and a nonword rhyme task (Do leat and kete rhyme?)

In all the imaging results, the authors looked at the rhyming> line match contrast*. (For an explanation of contrasts and subtraction logic in fMRI, see this post). Both groups had brain regions that changed in activation with age. However, the regions were different. In normal readers, the left anterior lateral occipital region (close to the visual word form area) became more active with age. In dyslexics, however, a more posterior region of the left occipitotemporal cortex became more active.

Developmental patterns in the front of the brain were also different. Normal readers showed an activation decrease in the right middle frontal/superior frontal region while dyslexic readers showed a decrease in the right superior frontal region.

The authors also looked at asymmetry. In normal readers (but not dyslexic), activity in the anterior lateral occipitotemporal region became increasingly asymmetric with age.

From these results, it appears that dyslexic readers aren’t just delayed versions of normal readers. Different regions are developing in each group, and the two groups are learning to use different brain regions to perform the same task. What does this mean? Different strategies? Compensatory processing? Hrmm…

Addendum: Careful readers might notice that there are some differences between these results and other papers I’ve discussed. Brown 2004 found an increase in left inferior frontal regions with age, but this paper only found it in dyslexic readers. Brown also found decreases in left extrastriate regions, while this group found increases. This could be due to the different tasks or subject variation.

*I’d be curious to see the correlations with age and task activations separately rather than just the rhyme>match contrast. It’d be interesting to see whether these correlations are due to changes in rhyming activation, line match, or both.



Shaywitz BA, Skudlarski P, Holahan JM, Marchione KE, Constable RT, Fulbright RK, Zelterman D, Lacadie C, & Shaywitz SE (2007). Age-related changes in reading systems of dyslexic children. Annals of neurology, 61 (4), 363-70 PMID: 17444510

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Dyslexia and Brain Connectivity: Insights from Periventricular Nodular Heterotopia

Accessibility Level:  Intermediate

One theory of dyslexia is that it stems from abnormal brain connectivity -- that faulty connections between different language areas result in reading difficulty. Now, some evidence from another condition offers some support for this theory.

Periventricular nodular heterotopia (PNH) is a neurological condition in which neurons don’t migrate to the correct location during brain development. Instead of moving to the cortex where they belong, neurons stay close to the ventricles, the fluid filled cavities in the center of the brain. The results in tiny nodules of gray matter along the ventricles, hence the name of the condition. People with PNH tend to suffer from adolescent onset epilepsy, although their intelligence and cognitive functioning is within the average range.



Bernard Chang and colleagues found that a strikingly large proportion of PNH patients had low scores on reading related tests, specifically reading fluency (timed reading) and rapid naming (remember the previous post on rapid naming?). In a smaller proportion of patients they also observed a deficit in processing speed.

In addition to behavioral measures, the authors also used diffusion tensor imaging to measure the integrity of the white matter tracts that link different brain regions. They found that white matter integrity in the PNH patients was correlated with reading fluency.

But wait, PNH has to do with nodules of gray matter near the ventricles. What does that have to do with white matter integrity? It turns out that these gray matter nodules are disrupting the nearby white tracts. Fiber tracts deviated around these nodules, and no fiber tracts projected into or from them.

There are certainly limitations to the conclusions we can draw from one study. The study is correlationial by nature, so it can’t prove whether the connectivity issues cause the reading difficulties. Also, it remains to be seen whether and how PNH patients differ from dyslexic people without PNH. But these are interesting findings. Yet another piece of the puzzle.


Chang, B., Katzir, T., Liu, T., Corriveau, K., Barzillai, M., Apse, K., Bodell, A., Hackney, D., Alsop, D., Wong, S., & Walsh, C. (2007). A structural basis for reading fluency: White matter defects in a genetic brain malformation Neurology, 69 (23), 2146-2154 DOI: 10.1212/01.wnl.0000286365.41070.54

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Is Dyslexia a Visual or Phonological Deficit?

It's interesting how the public's impression of dyslexia differs from the impressions of researchers in the field. I recently read an article by Vidyasagar and Pammer arguing that dyslexia is a visual deficit. To the general public, this claim seems obvious because most people believe that people with dyslexia see things backwards.



Many dyslexia researchers, however, will find this claim unusual, if not controversial.  This is because the field concluded long ago that the "reading backwards" theory is a myth. As we touched on in a previous article, all children will write backwards to some extent, not just dyslexic children

The current prevailing theory of dyslexia is actually poor auditory or phonological processing. People with dyslexia score lower than controls on phonological awareness, and phonological awareness in children predicts reading skill later on. In addition, some phonological training studies have succeeded in improving reading performance in children with dyslexia.

Vidyasagar and Pammer propose a different theory.  They argue that dyslexia is due to a deficit in visual attention and the dorsal visual stream.

Human vision is processed in two pathways in the brain. The ventral stream processes object identity, and the dorsal stream processes object location. When we read, the dorsal stream helps us direct our attention smoothly from one word to the next. Vidyasagar argues that a deficit in the dorsal stream is the underlying cause for dyslexia.

They cite some evidence for causality. Studies have found that deficits in coherent motion detection and visual contrast sensitivity, both dorsal stream functions, predict future reading skill.

But what about all the phonological results? Vidyasagar argues that  both visual and auditory input are required to develop a good understanding and awareness of phonetics, and that the  phonological deficits in dyslexia arise from a lack of high quality visual input during the time period when phonological awareness is maturing.  It will be up to researchers to conduct comprehensive studies to test this.

So what do I think?  My favorite theory is neither purely phonological nor visual one, but rather another possibility that Vidyasagar touches on – a deficit in rapid sensory processing. Rapid auditory processing is important for phonological awareness, and rapid visual processing is important for visual attention and visual search.  There is evidence that people with dyslexia have deficits in both.

Genetically, the theory makes sense. Genes don’t just code for one function. More often, they code for proteins that show up in multiple systems. You could imagine a gene that codes for some rapid processing neuron that plays a roll in both visual and auditory processing, resulting in the complex disorder we know as dyslexia.

It’s an exiting time to be a brain researcher.

Vidyasagar, T., & Pammer, K. (2010). Dyslexia: a deficit in visuo-spatial attention, not in phonological processing Trends in Cognitive Sciences, 14 (2), 57-63 DOI: 10.1016/j.tics.2009.12.003

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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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Color and Object Naming Speed Predicts Future Risk for Dyslexia

An important goal for any developmental disorder research is early detection. The earlier the detection, the earlier we can start intervention and treatment. Dyslexia is tricky though. It’s a reading disorder, and by definition cannot be diagnosed until reading instruction begins. However, we can still look for signs that predict future risk for dyslexia.

 One predictor of future dyslexia is rapid automatized naming (RAN) speed. A RAN test consists of naming an array of objects, colors, letters, or symbols as quickly as possible. It makes sense that letter and symbol naming speed (also called alphanumeric RAN) might predict reading skill. Surprisingly however, speed at naming pictures of objects and color patches also predicts future reading skill. Lervag and Hulme (Psychological Science 2009) studied this in a longitudinal study of Norwegian schoolchildren.


The idea was to test children before they learn to read and look for test results that predicted future reading skill. Formal reading instruction in Norway begins in second grade. Therefore, Lervag and colleagues conducted several tests on first graders. These tests included RAN and phoneme awareness (tasks like picking a word that begins with a certain sound). They then retested the students in second, third and fourth grade on these skills as well as reading fluency. They found that performance on nonalphanumeric RAN in first grade predicts phoneme awareness and reading fluency later on.

Lervag and colleagues investigated this finding in further detail by separating the RAN response times into articulation times and pause times between words. They found that the pause times were a much better predictor of future reading performance than articulation times. It should be noted though, that articulation times were much less variable than pause times, with a standard deviation of around 4.5 ms rather than about 16 ms.

These are pretty interesting results, and it raises the obvious question -- what is it about rapid color and object naming that predicts future reading skill? I can think of several possibilities.

1. Lervag suggests that reading may be tapping into the same pathways used for object recognition and naming. Remember the mirror invariance paper from last week that suggested the visual word form area might be involved both in word and object recognition? It could be that both reading and object naming are served by the same brain areas.
Both reading and rapid naming involve seeing an object, retrieving its phonological representation, and outputting the phonology via motor routines. Perhaps performance in RAN reflects the strength of the connections between visual, phonological, and speech areas.

2. Attentional focusing could be another factor. Rapid automatized naming requires directing visual attention from one object to the next in a controlled manner, and reading requires the same skill. Perhaps a deficit in attentional focus could be the underlying factor.

3. An even lower level explanation would be visual motor control. Both RAN and reading require controlled eye movements from one item to the next. Deficits in motor control have been reported in dyslexia, but I don’t know of any reported eye movement deficits. If anyone knows more about this, do let me know.

What do you think is the connection between RAN and dyslexia?

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