Educational implications of brain-function tests
Do neurological differences help identify autism, ADHD, dyslexia, or hearing loss and guide instruction for them?
Given how fascinated we humans are with our brains and minds, it is no wonder that we might be curious about whether some objective measure of neurological activity might provide an indicator of problems. What if we learned that individuals with Condition X regularly differed from those who do not have Condition X in some way that can be measured objectively (e.g., physiologically), not subjectively (e.g., self-report)? And, if we knew somethings about how that objective measure could be related to behavior—well—we might be tempted to examine co-relationships—Do kids who have a lot more (or a lot less) activity on Physio-measure A than their peers also have a lot more (or less) Behavior R?
Researchers have had lots of measures of neurological structure and function to examine. A few examples (I hasten to note that this is not a comprehensive review):
Is the flow of blood in the brains of individuals with dyslexia greater in some areas and lesser in other areas than the flow of blood in the brains of their non-disabled peers, especially when both groups are doing literacy like tasks (Brown et al., 2001; Shaywitz et al., 1998; see summary by Shaywitz & Shaywitz, 2005)?
Are the brains of children with autism bigger than those of some peers who have related disorders (Hardan et al., 2001; Sparks et al., 2002)? Are certain aspects of these brain differences associated with, for example, cognitive differences (Webb et al., 2009) or repetitive behavior (Estes et al., 2011)?
Do the patterns of peaks and valleys in brain activity along the neural pathway from the inner ear to other parts of the brain differ for individuals with certain disorders (e.g., hearing loss, autism, ADHD) in comparison with their peers (Talge et al. 2022; Young et al., 2022), especially when associated with auditory stimuli (“chirps” or “clicks”)?
Overall, we can say that research on neurological function using different methods and examining different aspects of the brain provides evidence that people’s brains work differently. Indeed, it seems possible to describe the brains of individuals with clinically defined disorders (as specific groups, e.g,. ADHD) appear to be different from the brains of others. What is more, sometimes researchers have found that those differences appear connected to some more specific behaviors (e.g., phonemic awareness).
Implications for identification or diagnosis
As measures of neurological function become more refined, they can be more useful in disagnosing difficulties. In assessments of infants who might have hearing loss, for example, the auditory brainstem response described in the third bullet has become a valuble tool. The American Speach and Hearing Association provided a simple, clear Web page about it.
In many other cases, however, the diagnostic specifity and sensitivity (to mention two issues of many possible issues regarding assessment) has not been fully established. That is, researchers do not know exactly how extensively the neurological measures correlate with behavior and, therefore, categories or groups based on behavior. Said another way: An atypical result on a measure of auditory brainstem response may or may not indicate the presence of autism. That’s a step too far (Talge et al., 2022).
Implications for practice
An obvious hope, of course, is that if we could just change these brain functions, we could affect the problems with which they are associated. Here’s the good news: We can change those brain functions.
But, not with electrical stimulation. And drugs aren’t necessary. Those interventions are barking up the wrong trees.
We change them by teaching effectively. We have pretty dang strong evidence, for example, that teaching decoding (i.e., sounding out words automatically) changes the flow of blood in learners’ brains. That’s it! Changing the behavior causes changes in the brain (Blachman et al., 2004; Shaywitz et al., 2004; see Barquero et al., 2014, for a review). It is likely that methods matter; some methods, such as those I described in an earlier post have been shown to make those differences.
Indeed, some instructional methods produce more substantial changes than other methods. I, of course, want to argue that we should use those methods (Lloyd et al., 1998) that produce better outcomes.
Anyway, all this brain stuff is pretty fascinating, but we wind up right back at the same place: Teach effectively!
Barquero, L. A., Davis, N., Cutting, L. E. (2014). Neuroimaging of reading intervention: A systematic review and activation likelihood estimate meta-analysis. PLoS ONE, 9, e83668-16. https://doi.org/10.1371/journal.pone.0083668
Blachman, B. A., Schatschneider, C., Fletcher, J. M., Francis, D. J., Clonan, S. M., Shaywitz, B. A., & Shaywitz, S. E. (2004). Effects of intensive reading remediation for second and third graders and a 1-year follow-up. Journal of Educational Psychology, 96(3), 444-461. https://doi.org/10.1037/0022-0618.104.22.1684
Brown, W. E., Eliez, S., Menon, V., Rumsey, J. M., White, C. D., & Reiss, A. L. (2001). Preliminary evidence of widespread morphological variations of the brain in dyslexia. Neurology, 56, 781-783
Estes, A., Shaw, D. W., Sparks, B. F., Friedman, S., Giedd, J. N., Dawson, G., Bryan, M., & Dager, S. R. (2011). Basal ganglia morphometry and repetitive behavior in young children with autism spectrum disorder. Autism Research, 4(3), 212-220. https://onlinelibrary.wiley.com/doi/pdf/10.1002/aur.193
Lloyd, J. W., Forness, S. R., & Kavale, K. A. (1998). Some methods are more effective. Intervention in School and Clinic, 33(4), 195-200. https://doi.org/10.1177/105345129803300401
Shaywitz, S. E., & Shaywitz, B. A. (2005). Dyslexia (specific reading disability). Biological Psychiatry, 57(11), 1301-1309. https://doi.org/10.1016/j.biopsych.2005.01.043
Shaywitz, B. A., Shaywitz, S. E., Blachman, B. A., Pugh, K. R., Fulbright, R. K., Skudlarski, P., Menci, W. E., Constable, R. T., Holohan, J. M., Marchione, K. E, Fletcher, J. M. Lyon, G. R., & Gore, J. C. (2004). Development of left occipitotemporal systems for skilled reading in children after a phonologically-based intervention. Biological Ppsychiatry, 55(9), 926-933. https://doi.org/10.1016/j.biopsych.2003.12.019
Shaywitz, S. E., Shaywitz, B. A., Pugh, K. R., Fulbright, R. K., Constable, R. T., Mencl, W. E., Shankweiler, D. P., Liberman, A. M., Skudlarski, P., Fletcher, J. M., Katz, L., Marchioe, K. E., Lacadie, C., Gatenby, C., & Gore, J. C. (1998). Functional disruption in the organization of the brain for reading in dyslexia. Proceedings of the National Academy of Sciences, 95(5), 2636-2641. https://www.pnas.org/doi/epdf/10.1073/pnas.95.5.2636
Sparks, B. F., Friedman, S. D., Shaw, D. W., Aylward, E. H., Echelard, D., Artru, A. A., Marvilla, K. R., Geidd, J. N., Munson, J., Dawson, G., Dager, S. R. (2002). Brain structural abnormalities in young children with autism spectrum disorder. Neurology, 59(2), 184-192. http://www.neurology.org/cgi/content/full/59/2/184
Talge, N. M., Adkins, M., Kileny, P. R., & Frownfelter, I. (2022). Click-evoked auditory brainstem responses and autism spectrum disorder: A meta-analytic investigation of disorder specificity. Nature: Pediatric Research, 92, 40–46. https://doi.org/10.1038/s41390-021-01730-0 or https://rdcu.be/cUpnb
Webb, S. J., Sparks, B. F., Friedman, S. D., Shaw, D. W., Giedd, J., Dawson, G., & Dager, S. R. (2009). Cerebellar vermal volumes and behavioral correlates in children with autism spectrum disorder. Psychiatry Research: Neuroimaging, 172(1), 61-67. https://www.sciencedirect.com/science/article/pii/S0925492708000875
Young, A., Cornejo, J., & Spinner, A. (2022, January). Auditory brainstem response. StatPearls [Internet]. https://pubmed.ncbi.nlm.nih.gov/33231991/
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