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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The N1 Component in Second Graders

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Last week, we learned that the N1 component in normal reading adults differentiated between words and symbols, while the N1 component in pre-reading kindergartners did not. The question now is, at what point in development does N1 component start resembling that of adults? Maurer and colleagues tested the same kindergartners from their 2005 paper when the kids were in second grade to see how their brain activity changed after two years of reading instruction.



These were their findings:
1. The N1 component differentiates between words and simple strings in the second graders.
2. Second graders actually had a greater words/symbols N1 difference than adults.*
3. There is a correlation between N1 specialization and reading fluency. In other words, the difference in N1 amplitude between words and symbols was correlated with faster reading in the second graders.
4. The N1 negativity was more left lateralized in adults than in children. The N1 topography was bilateral for 2nd graders, and right lateralized in kindergareners.

Conclusions: Two years of reading instruction is enough for the brain to start differentiating between words and meaningless symbols. In terms of the development of N1 specialization, there are hints of a U shaped curve, with 2nd graders displaying even greater word/symbol differences than adults.

* Amplitudes in general were bigger in the second graders, but the difference held when amplitudes were normalized between children and adults


Maurer U, Brem S, Kranz F, Bucher K, Benz R, Halder P, Steinhausen HC, & Brandeis D (2006). Coarse neural tuning for print peaks when children learn to read. NeuroImage, 33 (2), 749-58 PMID: 16920367

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The N1 Component in Prereading Children

Accessibility: Intermediate-Advanced

Just to recap from the last article, the N170 is an ERP component that differentiates between words and symbol strings in normal reading adults. This the specialization developed after learning to read, or does it have something to do with the visual properties of symbols?



Maurer and colleagues tested pre-reading kindergartners to see whether the specialization is there before they learn to read. They had kids perform the same task as adults (looking at a series of words, pseudowords, symbol strings, and pictures).

They found several things:

1. Adults again had the same N170 (called N1 in this paper), which was stronger for words than symbols.

2. Kids also had an N1, but it was later, had a larger amplitude, and most importantly, did not distinguish between words and symbols, suggesting that this N1 specialization stems from experience with words.

3. Some of the kids, the ones with high letter knowledge, did have an N1 that differentiated between letters and symbols. However, the pattern was different from adults. While adults had the strongest effect on the left side of the brain, these children showed an effect on the right side.

So in conclusion, the N1 specialization seems to be related to reading. However, there seem to be some intermediate steps in the development of the specialization. At least in an early stage, the right hemisphere is involved, and then the processing becomes more left lateralized.


Maurer U, Brem S, Bucher K, & Brandeis D (2005). Emerging neurophysiological specialization for letter strings. Journal of cognitive neuroscience, 17 (10), 1532-52 PMID: 16269095

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Introduction to the N170 Response to Words

Accessibility:  Intermediate-Advanced

This month is N170 month. I'm going to be going through a bunch of papers by Urs Maurer on the N170 ERP component and how it relates to word processing. EEG is not my specialty, so hopefully I won't mess anything up.



For this post, we'll start with the basics. The N170 is an ERP component measured in EEG experiments. The N means that it is a negative potential, and the 170 means that it peaks roughly at around 170 ms, although the timing can vary. The N170 tends to be elicited by certain categories of visual images (like faces), and is enhanced for categories for which the subject has some expertise (for example, enhanced N170 response for bird experts when viewing birds).

This last characteristic makes the N170 helpful for studying word processing. Urs Maurer and colleagues tested adults by showing them words, pseudowords, and symbol strings*. The adults showed a greater N170 to words than symbol strings, which would be consistent with an expertise for words acquired over years of reading. The N170 was also more left lateralized for words than to symbol strings, which is not surprising given the general left lateralization of language. Also, the N170 seems to be stronger over the inferior occipital temporal channels, close to the visual word form area.

So those are the basics for the N170 in normal reading adults. It's a useful tool for studying word processing in populations like children and people with dyslexia, so that is where we will continue.

*the task was to detect repetitions


Maurer U, Brandeis D, & McCandliss BD (2005). Fast, visual specialization for reading in English revealed by the topography of the N170 ERP response. Behavioral and brain functions : BBF, 1 PMID: 16091138

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Brain Measures Predict Future Improvement in Children With Dyslexia

Accessibility: Intermediate

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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Don't Assume that fMRI and MEG Will Give You Comparable Results

Accessibility: Intermediate/Advanced

There are three common methods of studying brain function in normal human populations: fMRI, MEG, an EEG. There is surprisingly little crosstalk between the techniques, mostly due to practical issues.For better or worse, labs tend to specialize in one technology.

It's often assumed that the relationship with techniques is straightforward, that it's simple to map results from one technique onto another. However, a recent study by Johanna Vartianen and colleagues suggests otherwise.



The group wanted to study reading using all three brain techniques. Participants performed the same experimental paradigm twice: once with simultaneous EEG and fMRI, and once with simultaneous EEG and MEG. Participants saw words, pseudowords, consonant strings, and symbol strings, and words embedded in noise. Their task was to detect immediate repetitions. The EEG results from the two sessions were comparable, so the researchers went on to compare the fMRI and MEG activation patterns for the experiment.

To summarize, activation patterns between MEG and fMRI did not show a straightforward relationship. In some regions, the two techniques showed the same pattern. For example, in the occipital lobe, both MEG and fMRI measures had more activation to noisy words than other types of stimuli.

If you look at the occipitaltemporal lobe however, the two techniques had opposite results. MEG showed more activation to real letters than symbols, while FMRI showed more activation to symbols then letters.

In the left frontal cortex the two regions had completely different patterns. FMRI activation was higher for words and pseudowords than symbols and noisy words. The MEG results showed no difference at all between stimulus types.

I guess this is one of these results that you don't see going in, but in hindsight make you hit yourself over the head. FMRI and MEG measure very different things, so it’s entirely possible that results would come out differently. FMRI measures cerebral blood flow on a timescale of several seconds, while MEG measures synchronous electrical activation with millisecond resolution. So ( as the authors suggest) non-synchronous activity may be lost in MEG. Meanwhile, fMRI picks up average activity over a longer time period and may miss short-term activity.

Interestingly, the authers mentioned that previous MEG results for the visual word form area were fairly robust to task differences, while fMRI results do seem to vary with task. Now I don't know the MEG literature well, but they're certainly right about the fMRI literature. In that case, I wonder what it is about the MEG that makes its results relatively task independent. Is it the better temporal resolution? Perhaps MEG analyses focus on early, bottom up processing, which may be relatively task independent?


Vartiainen J, Liljeström M, Koskinen M, Renvall H, & Salmelin R (2011). Functional magnetic resonance imaging blood oxygenation level-dependent signal and magnetoencephalography evoked responses yield different neural functionality in reading. The Journal of neuroscience : the official journal of the Society for Neuroscience, 31 (3), 1048-58 PMID: 21248130

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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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