Dyslexic vs. Nonimpaired Readers: Differences in Brain Development

Wednesday, March 31, 2010


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


fMRI Subraction Analysis and Why it Matters

Tuesday, March 30, 2010

Accessibility:  Basic

Let’s say you wanted to do an experiment about color processing. We could do the following:

1. Roll someone into the scanner.
2. Show them two colors
3. Have them press the button corresponding to the color they prefer.
4. Look at the resulting activations, and voila, we have the “color preference area.”

But it’s not that simple. The brain is very active, even when supposedly at rest. While performing the task described, the subject is also breathing, processing ambient noise, thinking about grocery shopping, as well as who knows what else. How do you tell what activation is due to the color judgment, and what is due to other processes?

The traditional fMRI solution is to compare activation with a baseline condition. For our example experiment, we may want a comparison condition where the subject sees the same images, but presses a random button rather than picking a color. We then take the activation from this comparison condition and subtract it form the condition we’re interested in. The assumption (and it’s an assumption, meaning that it may not always be true) is that we’re subtracting out irrelevant brain activation– for example, brain activation due to seeing colors, pressing buttons, being inside a scanner, etc.

This is important to keep in mind when evaluating fMRI results. If someone tells you that brain region X is active during a certain task, you always want to ask what the comparison condition is. If region X is active during task Y, but the comparison condition is super simple (say just laying there in the scanner, for example), that’s not very impressive – lots of other regions will be active in that comparison, and it may not be simply due to that task.


Development of Modality Tuning During Reading and Repetition

Monday, March 22, 2010

Accessibility Level:  Intermediate/Advanced

Today we’re again looking at the theme of increasing specialization in the brain over development. Rather than specialization in terms of spatial extent, as touched on in Brown 2004, Cerebral Cortex, this paper’s finding suggests specialization in processing of sensory modalities.

Church and colleagues tested children (age 7-10) and adults (18-35) in a word generation task. During the experiment they read words off a screen and repeated words presented aurally. Like the two papers previously discussed here by this group, the authors matched for behavior between children and adults.
They authors report several findings.

1. First, most brain regions did not change in activation over time. In well known language areas like the inferior frontal gyrus and superior temporal gyrus, the authors found no difference between children and adults. Also, they found no differences in lateralization (how much one side of the brain was favored over another) between children and adults.

2. The regions that differed between the two groups were mainly extrastriate visual regions, and all regions with differences had greater activation for children than adults. Unlike the Brown 2005 paper, where some frontal regions were found to have greater activation in adults, this paper found no such regions. This could be due to the different task (word generation vs. reading/repeating), or variation in the participant pools of the two the studies.

3. In several visual regions, including a cluster very close to the visual word form area, adults had more activation to the visual presentation than to auditory presentation, while children had similar activation to the two modalities. This suggests that these areas might be more specialized for the visual modality in adults. In other words, the region gets “tuned” to the visual modality as the children mature (However, the interaction between modality and age was not statistically significant).  The authors propose several possible mechanisms responsible for this modality tuning difference. Perhaps the kids are using a different strategy, visualizing more during the auditory task. Or perhaps their brains are just organized differently.

Together with the Brown 2004 paper, this paper presents an interesting story about increasing specialization and efficiency in the maturing brain, in which the immature brain starts out with relatively nonspecialized brain regions and recruits more brain regions to accomplish the tasks at hand. Then, maturation and expertise result in more specialization, finer tuning, and fewer recruited regions.

Church JA, Coalson RS, Lugar HM, Petersen SE, & Schlaggar BL (2008). A developmental fMRI study of reading and repetition reveals changes in phonological and visual mechanisms over age. Cerebral cortex (New York, N.Y. : 1991), 18 (9), 2054-65 PMID: 18245043


Dyslexia and Brain Connectivity: Insights from Periventricular Nodular Heterotopia

Monday, March 15, 2010

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


Brain Change Patterns in Developing Children

Monday, March 8, 2010

Accessibility Level: Intermediate-Advanced

What changes in the brain as children mature? Are there patterns in the way the changes occur? Do some regions mature more quickly than others?

Last time, we talked about a paper by Schlaggar et al that examined brain differences between children and adults during a word generation task. A study published in Cerebral Cortex by Brown and colleagues extends that study, looking at changes in more detail.

The authors scanned children and adults aged 7-32 while performing a word generation task. Participants were given a word, either visually or aurally, and had to say a response based on an instruction ("opposite" for example). The authors then looked for the regions that differed in activation between the youngest (7-8) and oldest groups (23-32). For more details on this, and how they controlled for performance differences, see the entry on the Shlaggar et al paper.

The authors found many regions that either increased or decreased in activation between ages 8 and 23. That’s not surprising. It’s a lot of years and a lot of development. They did notice a few patterns though.

1. Posterior regions, generally involved in sensory processing, tended to decrease in activation with age. Frontal regions, generally involved in controlling and modifying the activity of the lower level regions, tended to increase with age.

2. The posterior and frontal regions differed not only in direction of change, but in speed of change. The frontal regions became more adult-like first, with the posterior regions maturing later.

The authors propose a model explaining the results. In this model, children first use lower level, sensory regions to perform a task. Because they are unskilled, their brains are less efficient and activate more. Then, as the children mature, frontal control regions develop and kick in, at which point they help fine-tune the posterior regions. The posterior sensory regions then become more efficient, and activation in these regions decreases.

This is an interesting model, and it will be interesting to see whether future developmental studies confirm it.

Brown, T. (2004). Developmental Changes in Human Cerebral Functional Organization for Word Generation Cerebral Cortex, 15 (3), 275-290 DOI: 10.1093/cercor/bhh129


Comparing Child and Adult Brains: How to Account for Performance Differences?

Wednesday, March 3, 2010

In an ideal world, we’d be able to study maturational brain changes by scanning a group of adults, a group of children, and comparing the brain images. Unfortunately, there are complications.

One complication is that these studies usually require doing some kind of task in the scanner, and children usually have lower accuracy and longer reaction times on this task. These differences, especially reaction time differences, can have a significant effect on brain activation. (Activation is averaged over seconds, so the longer your reaction time, the higher the brain activation, simply because you spend more time on the task). So how do we know what differences are due to actual brain maturation and what differences are due to poor performance inside the scanner?

In a 2002 study, Schlaggar and colleagues addressed the issue of performance differences by comparing only those children and adults that performed similarly on the task. They were interested in word processing in children (aged 7-10) and adults (age 18-35). In their experiment, participants saw single words on a screen and had to say a response word based on a cue (for example, a rhyming word, or the opposite word).

Instead of comparing all adults and all children, Schlaggar and colleagues divided the participants into two subgroups. The top scoring children and lower scoring adults to formed a Performance matched subgroup, where the children’s performance did not differ significantly from adults. The rest formed a Performance Non-matched subgroup, where there were clear differences in accuracy and response time between adults and children.

Schlaggar and colleagues looked at several regions of interest in the left frontal and left extrastriate regions, all traditional language and word processing areas. When there were differences between age groups, the children almost always had greater activation.

But the interesting results occur when you compare the subgroups. Some regions showed differences in the Non-Matched subgroup that disappeared when you look at the Performance Matched groups, suggesting that the brain differences here were due to performance differences inside the scanner.

Other regions, however showed differences between children and adults in both the Performance Matched and the Performance Non-matched subgroups. In these regions, one can safely assume that there’s more to these differences than simple in scanner performance.

What does this tell us? For one thing, it tells us that in-scanner performance differences between children and adults should not be ignored. There were several “developmental” differences here that disappeared as soon as you controlled for in-scanner performance. On the other hand, there do appear to be differences that remain even when you have children and adults that perform equally.

A few things to think about with this study. First, it’s admirable that the authors control for performance, but doing so also introduces opposite selection biases in children and adults. You have to wonder what population of children would perform as well as adults almost twice their age, and conversely, what population of adults would perform at the level of 7-10 year olds. Is it fair to compare these two groups and generalize these comparisons to the entire population?

Second, what does it mean to control for in-scanner performance? If we treat it as a confounding factor and control for it, we’re assuming that performance differences on this word generation task are irrelevant to the process we’re studying. However, that can’t be completely true. We’re interested in word processing differences between children and adults, so if we look for children and adults that perform similarly on a word generation task, we’re filtering out some of the differences that we set out to study.

Third, it might be helpful, as mentioned in BJ Casey’s commentary on the study, to differentiate between differences from maturation alone and differences due to skill level. In a field such as reading, this might be hard to tease apart. While children’s brains mature between ages 7 and 18, they also undergo thousands of hours of reading instruction that introduce changes in the brain. When we study reading acquisition in children, therefore, we should think about kind of brain changes we’re interested in, and that will affect the comparisons and analyses we do.

Schlaggar BL, Brown TT, Lugar HM, Visscher KM, Miezin FM, & Petersen SE (2002). Functional neuroanatomical differences between adults and school-age children in the processing of single words. Science (New York, N.Y.), 296 (5572), 1476-9 PMID: 12029136

Casey, B. (2002). NEUROSCIENCE: Windows into the Human Brain Science, 296 (5572), 1408-1409 DOI: 10.1126/science.1072684


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