Our Cognitive Fingerprint: A structural look at autism and dyslexia

Our Cognitive Fingerprint: A structural look at autism and dyslexia

Written by the Graduate College member, Jennifer Plosz. She is currently a graduate student in the faculty of Education, researching the role that visualization and images might play in the growth of mathematical understanding with a particular interest in students who are non-typical learners.

No two brains are alike, yet it can not be denied that with diversity there are also trends and similarities. Each of us has a unique brain fingerprint. Just as fingerprints have their own unique pattern made up of grooves and ridges, so to do our brains. Yet, even our very unique fingerprints can be categorized based on similar patterns, such as the three basic overarching fingerprint designs: Whorl, Arch, and Loop.

Another example is if we look at the physiology of our hands, no two hands are exactly alike. Yet, if we focus attention on the width and length of our fingers, some of us have quite short stubby fingers, some have very long slender fingers, and the rest of us lie somewhere in between. The structure of our hands can have an impact on the types of tasks we are more suited towards. For example, those who have sausage-like fingers may struggle to do certain fine delicate detail work but provide an advantage in tasks requiring strength. Whereas those with slender fingers may struggle with tasks that require more strength but find delicate precision type tasks easy. In the study of brain physiology, we can likewise make similar categorizations to help us notice some broader trends. Just as some fingers are wide and some narrow, so it is with our brains, some brains are made up of wider folds, some have more narrow folds, and the rest fall somewhere in between. Could our structural brain distinctions impact our cognitive areas of strength and weakness as well?

Dr. Casanova’s work is in studying the brain physiology of different cognitive profiles. Over his many years of research, he has come to believe that there are two cognitive profiles that stand at either end of a spectrum from each other (Williams & Casanova, 2010); Autism being at one end and dyslexia at the other end, with what may be considered “neurotypicals” falling somewhere in-between. Meaning that some people may present closer to the autistic end of the spectrum, as they tend towards being more detail oriented, craving routine, and excellent rote memorizers, while another person, dyslexics, may be more conceptually oriented, crave difference and struggle to rote memorize (Perrachione, et al., 2016).

The structural differences in these two brain physiologies appear to have opposite characteristics. The brain of a person with autism has more folds in the brain than a “typical” brain (see illustrative explanation below). This increase in folds causes the folds to be more narrow, which makes the white matter area held within the fold to be denser than a “typical” brain. This affects how their neural connections are created. Autistic brain structures create more local connections than a “typical” brain structure and fewer long-range connections which also causes them to have fewer connections between the two hemispheres of the brain than “typicals”. This affects how people with autism think and learn. They tend to be very detail oriented and can develop very specialized skills, but struggle to make some of the larger or big picture connections. A person with autism often struggles to see the forest for the trees.

This tendency can play out well in school for a period of time as students with autism are great at picking up detail and spitting back the information exactly how it was presented to them. The teacher can then believe that the student is understanding. The struggle for the student with autism in school is

checking for a deep understanding. Are they making the connections that they should be making? Often the struggle is highlighted when given a bigger project, they may focus too intently on one aspect of the assignment or veer off track on something very loosely connected, missing the main overarching point to the assignment.

The dyslexic brain, on the other hand, has fewer folds than the “typical” brain (see illustrated explanation below). This decrease in folds causes the folds to be wider, which makes the white matter area held within the fold more spacious. This affects how their neural connections are created. Dyslexic brain structures create more long-range connections than a “typical” brain structure and fewer local connections. More long-range connections also cause them to have more connections between the two hemispheres of the brain than “typicals”. This affects how dyslexics think and learn, they tend to be very conceptual, big-picture thinkers, who often make connections between ideas that others do not, but they struggle to learn the details. They also tend to crave difference and creativity but are very weak rote memorizers (Perrachione, et al., 2016). They struggle to see the trees for the forest, but if they are not given the forest/big picture/conceptual aspect of a topic they are often left without a starting point, which is the root of a lot of their struggle with school.

Mimicry is alive and well in the classroom. The teacher presents the material and the student is expected to give that information back to the teacher in exactly the same form as it was presented. Students who can do this successfully often receive top marks. Dyslexics struggle to retain the details and specifics when presented with material in this way. However, they are often quite adept at taking that information and then synthesizing it or connecting it to something else. Yet, many classrooms do not focus on the critical thinking aspects of a subject. In mathematics for example, when the material is presented as a procedure that should be mimicked, the dyslexic will struggle; yet if that same topic was taught through relational understanding, the dyslexic would excel. The dyslexic mind must understand first, and then they gradually learn the details. Yet, often in school when students struggle with a topic, there is a belief that because the student is struggling they are not intelligent. This in combination with the cultural belief that memorization is easier than conceptual understanding, often prompts the teacher to breaks down the topic into little steps that the student is to memorize. This assumption of lack of intelligence and cultural belief in the ease of memorization creates an environment for dyslexics that is a downward spiral of constant challenge in school and contributes to their own belief in their lack of intelligence and ability.

It should also be noted that Dr. Casanova did not find one particular region of the brain in all people with autism or all people with dyslexia to be more frequently affected than any other, hence the fingerprint analogy. For example, it is a commonly held belief that all dyslexics struggle with phonemic awareness. One would then expect that this area of the brain associated with phonemic awareness would be affected by wider brain folds in all dyslexics. Yet, Dr. Casanova did not find this to be the case. The area of the brain that deals with phonemic awareness were not more or less affected in all the dyslexic participants. Rather, he found there to be no pattern when it came to any one area as being more affected by wider folds than others, he just found variation throughout the participants. So, some areas within the dyslexic brains were more affected than others, but as a whole group, one area was not found to be consistently affected. This seems to support the extreme level of variation that is seen in how each student with these profiles presents areas of strength and weakness. Yet, there are still overarching characteristics that can be found within each of these groups.

These ideas of spectrum and variation create an interesting discussion in many areas of the dyslexia and autism debate. Does this influence commonly held beliefs in right brain dominance for dyslexics? A University of Utah study (Nielsen, Zielinski, Ferguson, Lainhart, & Anderson, 2013) looking at over 1,000 brains and discovered similar findings of variation, but in areas of brain activation rather than physiology. They found that none of their participants demonstrated preferential activation in one hemisphere over the other. Yet, there does seem to be evidence of left-handed people having more brain symmetry which would lead to better communication between the right and left sides. This connects well with Dr. Casanova’s work around dyslexics having more connections from one hemisphere of the brain to the other as dyslexics have a greater tendency to be left-handed.

Ideas of phonemic awareness and dyslexia may also spark discussion in the context of this found variation. Is phonemic awareness the root of dyslexia or is it weak neural adaptation (struggle to automatize/rote memorize) (Perrachione, et al., 2016)? Is the weakness for phonemic awareness attached to a weakness in that portion of the brain? Maybe for some. However, for others the issue may be more related to rote memorizing which sounds go with which letters. This is a very challenging task if you struggle to memorize. If we think about the English language, there are 26 letters that look nothing like a sound and then we take some of those letters and put them in a sequence, such as ‘ough’, which in English presents with seven different sounds (ie. through, cough, enough, dough, bough, borough, bought) or if you look at the sound ‘oo’ that has five different spellings (ie. root, ruin, rude, new, through). Retaining the look of a word attached to its pronunciation or meaning is, therefore, an extreme task in rote memorization.

And finally, how does this idea of spectrum fit with those who claim to have both autism and dyslexia? Do these individuals have both narrow and wide brain folds affecting different parts of the brain? Or does their autistic brain physiology present with some dyslexic tendencies with the root really being autism? Or vice versa?

There are many aspects of these profiles left to be discovered and re-examined. However, Dr. Casanova’s research opens the discussion connecting learning patterns with brain physiology allowing us to better understand how different cognitive profiles have a tendency to make neural connections. This will hopefully have an impact on how we can more effectively engage with these students. They both have extreme areas of strength, but the apparent cost is then also extreme areas of weakness. Learning how to support them in their weakness, but more importantly tap into their strengths is crucial for each of these profiles to find success and become the extremely valuable contributors to our society that evolution intended them to be.

Illustrative explanation

In the diagram below, the grey outer lining is representative of the cerebral cortex which is made up of mostly grey matter. The cortex is where we form cognitive and language functions. Underneath the

grey matter is the white matter. The axons, thread-like parts, travel through the white matter area. It is along the axons that our neural impulses travel to connect to other neurons.

This visual interpretation was created by Jennifer Plosz based off information gathered from Manuel Casanova’s work. It is not meant to be a literal rendering, but rather an explanatory one – proportional differences are exaggerated.


Casanova, M. F., Araque, J., Giedd, J., & Rumsey, J. M. (2004). Reduced brain size and gyrification in the brains of dyslexic patients. Journal of Child Neurology, 19(4), 275-281.

Casanova, M. F., Buxhoeveden, D. P., Cohen, M., Switala, A. E., & Roy, E. L. (2002). Minicolumnar pathology in dyslexia. Annals of neurology, 52(1), 108-110.

Casanova, M. F., El-Baz, A. S., Giedd, J., Rumsey, J. M., & Switala, A. E. (2010). Increased white matter gyral depth in dyslexia: implications for corticocortical connectivity. Journal of autism and developmental disorders, 40(1), 21-29.

Casanova, M., El-Baz, A., Elnakib, A., Giedd, J., Rumsey, J., Williams, E., & Switala, A. (2010). Corpus callosum shape analysis with application to dyslexia. Translational neuroscience, 1(2), 124-130.

Elnakib, A., Casanova, M. F., Gimelrfarb, G., Switala, A. E., & El-Baz, A. (2012, July). Dyslexia diagnostics by 3-D shape analysis of the corpus callosum. IEEE Transactions on Information Technology in Biomedicine, 16(4), 700-708.

Perrachione, T. K., Del Tufo, S. N., Winter, R. M., Murtagh, J., Cyr, A., Chang, P., . . . Gabrieli, J. D. (2016). Dysfunction of rapid neural adaptation in dyslexia. Neuron, 92(6), 1383-1397.

Williams, E. L., & Casanova, M. (2010). Autism and dyslexia: A spectrum of cognitive styles as defined by minicolumnar morphometry. Medical Hypotheses, 74, 59-62.

Williams, E. L., El-Baz, A., Nitzken, M., Switala, A. E., & Casanova, M. (2012, March). Spherical harmonic analysis of cortical complexity in autism and dyslexia. Translational Neuroscience, 3(1), 36-40.

Posted on: January 17, 2018susanne

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