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Neurosci & Educ.--5 Days, 5 Ways. Day 5: Predicting Trouble

12/7/2012

 
This is Day 5 of my week-long series of posts on the use of neuroscientific data in educational practice.

There is another post today, summing things up. It's here.

Links to previous posts:

Challenges in applying neuroscientific data to education.
Day 1: Basic architecture
Day 2: Single cell inspiration
Day 3: Reliable neuro-knowledge
Day 4: Confirm a Construct

Today's technique differs from the other four. Those concerned how you could use neuroscientific data to improve a behavioral theory, which you would then use to improve education outcomes.

Neuroscientific data also shows promise in helping with the early identification of learning problems. The best-studied of these is dyslexia.

It would be very useful indeed to know with confidence which children will have difficulty learning to read. The earlier the intervention, the better.

Traditionally, one would use behavioral measures like word attack or reading fluency, or phonological processing. Typically, a battery of tests would be used. (One intriguing new study suggests that a measure of visuo-spatial attention may be a good predictor of later reading difficulty: Franceschini  et al, 2012)

But there is evidence that structural differences in the brains of children who will later have trouble learning to read are present before reading onset. (Raschle, Chang & Gaab, 2011; Raschle, Zuk & Gaab, 2012). dyslexia has a neural basis present before reading instruction begins, might you be able to identify children who will very likely have significant trouble with reading before instruction ever begins?

A number of laboratories have been working on this problem, and progress is being made.  These researchers are not looking to toss out behavioral measures--they are looking to supplement them. The more successful of these efforts (e.g., Hoeft et al., 2007) show that behavioral measures predict reading problems, neuroscientific measures predict reading problems, and using both types of data provides better prediction than either measure alone. In other words, the neuroscientific data is capturing information not captured by the behavioral measures, and vice versa.

This is not an easy problem to solve, but progress seems likely.




References

Franceschini, S., Gori, S. Ruffino, M., Pedrolli, K. & Facoetti, A.(2012). A causal link between visual spatial attention and reading acquisition. Current Biology, 22, 814-819.


Hoeft, F., Ueno, T., Reiss, A. L., Meyler, A., Whitfield-Gabrieli, S., Glover, G. H., ... & Gabrieli, J. D. (2007). Prediction of children's reading skills using behavioral, functional, and structural neuroimaging measures. Behavioral neuroscience, 121, 602-613.

Raschle, N. M., Chang, M., & Gaab, N. (2011). Structural brain alterations associated with dyslexia predate reading onset. Neuroimage, 57, 742-749.

Raschle, N. M., Zuk, J., & Gaab, N. (2012). Functional characteristics of developmental dyslexia in left-hemispheric posterior brain regions predate reading onset. Proceedings of the National Academy of Sciences, 109(6), 2156-2161.
Neau Deauze
12/7/2012 03:06:56 am

A very costly test which adds some additional predictive power may or may not be worth anything. Keep in mind, all measures are noisy, so a second behavioral test (especially one capturing slightly difference variance--but even the same one given a second time) would also provide additional predictive power, and at far lower cost.

Also note that a lot of these studies do not make it clear whether they are using cross-validation in an appropriate way. If the ROIs are selected to maximize predictive power, then predictiveness needs to be assessed on independent data, otherwise it's voodoo, as they say. Browse the brief and opaque method sections of these papers and see if you are completely convinced that this was done.


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    The goal of this blog is to provide pointers to scientific findings that are applicable to education that I think ought to receive more attention.

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