Dyslexia, Attention Deficity Hyperactivity Disorder and Autism

What is the nature of a pervasive developmental disorder of childhood? I usually tell my residents and fellows that “pervasive” in the context of diagnosis means that many different cognitive functions appear affected. In essence, to learn about autism is to learn about the whole of psychiatry. In order to read, understand, treat, and do research on autism a health professional must become conversant in many disparate fields such as language disorders, social skills, mental retardation, motor abnormalities, seizures, etc. The number of items necessary to gain a holistic understanding of autism grows exponentially once psychological and educational theories are included, e.g. theory of mind, imitation, weak central coherence, face recognition. It is therefore not surprising to discover that a significant proportion of autistic individuals have deficits in both reading and attention. This is the nature of a pervasive developmental disorder. But are these deficits the same as those seen in developmental dyslexia or attention deficit hyperactivity disorder (ADHD)?

Place a child with ADHD in a classroom and he will exhibit deficits in attention because his mind wonders. He/she has a fleeting attention. One moment he/she is focusing on the classroom chores, the next his mind travels to China where he/she is an adventurer in search of treasures. He or she can pay attention for short intervals of time and otherwise gets bored very easily. Place an autistic child in a classroom and he/she will also exhibit a deficit in attention. However, this time the inattention is prompted by an inability to make sense of the face of the teacher, is bothered by the buzzing and flickering of fluorescent lights, and/or gets irritable by an abnormally increased sensitivity to the texture of his/her clothes. Attention disorders verging on a pathological diagnosis, may be found in close to 30% of autistic individuals. Whether we can say that this implies comorbidity with ADHD is arguable.

Claiming that there is comorbidity across neurodevelopmental disorders based on a single behavioral symptom negates many aspects of the individuality of each condition. In this regard, there are marked differences in the cognitive styles of dyslexic or ADHD individuals and those within the autism spectrum. Dyslexics enjoy a top-down cognitive style, tend to be holistically-oriented and have a gestalt processing bias (e.g., they see the forest but lose track of the individual trees). They are considered to have strong central coherence and excel in synthesizing sensory or cognitive experiences. Individuals within the autism spectrum enjoy a bottom-up cognitive style which makes them detail-oriented. Thus, contrary to dyslexic/ADHD subjects, ASD individuals see the tree but tend to lose sight of the forrest. In addition, they have a local processing bias with weak central coherence and appear to be good analyzers.

The above related differences in cognitive style appear to have anatomical correlates. As compared to neurotypicals, dyslexics tend to have smaller brain volumes with a concomitant striking increase in the size of their corpus callosum (the white matter projections that join homologous areas in both cerebral hemispheres). In addition, they have a simplification of their convolutional pattern and their cortical modules for information processing (minicolumns) are wider than expected. We find completely the opposite in patients within the autism spectrum.

white matter projections
Figure legend: Connectivity within the brain is dictated by fibers of different lengths. Some of these join closely adjacent convolutions and are called arcuate fibers because of the shape of their trajectory. Other fibers bridge distant locations within the brain, including those of opposite brain hemispheres. Differences in cognitive styles appear dictated by a bias in the ratio of these short to long fibers.

Parcellation of white matter
Figure legend: Using computers different areal divisions of the white matter of the brain can be parcellated. The outer compartment of the white matter is composed primarily of short fibers that myelinate quite late in development. The inner compartment has longer fibers that myelinate early and are therefore functionally available at younger ages.

images
Figure legend: The volume of white matter inside of each gyrus has been called the gyral window. The volume seems to bear a direct relationship to the size of the corpus callosum, that is, a smaller gyral window is associated with a smaller corpus callosum and vice versa. In autism the gyral window and size of the corpus callosum are smaller while the opposite is described for dyslexia.

Many of the structural indices previously mentioned (e.g., convolutional patterns of the brain, the size of the corpus callosum) reflect the way different areas of the brain are connected together. Increased convolutional complexity, a smaller gyral window (the space at the base of the the convolution through which fibers going in and out of the cortex must pass), and a smaller corpus callosum all suggest a bias in connectivity favoring short connections at the expense of longer ones. This is the case for individuals in the autism spectrum where imaging studies have found an increase in volume for the outer white matter compartment that holds the fibers going from one convolution to the adjacent one. This connectivity pattern makes it easier to emphasize functions that can be done within a given region of the brain (e.g., finding embedded figures or working with block designs) but make it difficult to perform those functions in need of analyzing converging information from disparate brain regions (e.g., language, joint attention, face recognition). This is the connectivity pattern usually found in patients within the autism spectrum of disorders (ASD). It is not surprising that patients with attention deficit hyperactivity or dyslexia offer a pattern of connectivity that is diametrically the opposite.

cog_styles

I have previously focused some of my efforts in describing the mathematical relations among cells in minicolumns, the units of information processing within the brain. In this regard I have stated that, “…it appears that minicolumns exist within a phenotypic spectrum that intertwines the inhibitory-excitatory flow of neocortical information with a tweaking of the signal-to-noise ratio relevant to feature extraction” (Casanova et al., 2002). Because intra- and intercortical coordination is a finely tuned relationship of these signal-to-noise ratios,extremes at either end of a distribution can disturb the same functions, be it reading or attention. This can cause two people to exhibit similar skill profiles or deficiencies despite opposing underling pathologies. Making this distinction is clearly important, especially when considering possible therapeutic approaches or trying to explain the presence of special skills.

References

Casanova MF, Bushoeveden DP, Cohen M, Switala AE, Roy E. Minicolumnar pathology in dyslexia. Ann Neurol 52:108-110, 2002.

Williams EL, Casanova MF. Autism and dyslexia: a spectrum of cognitive styles as defined by minicolumnar morphometry. Medical Hypothesis 74:59-62, 2009.

Sokhadze E, El-Baz A, Casanova MF. Event related potential study of attention regulation during illusory figure categorization task in ADHD, Autism Spectrum Disorder, and typical children. Journal of Neurotherapy 16:12-31, 2012. (The article reviews the brainwave parameters that distinguish ADHD from ASD individuals)

10 responses to “Dyslexia, Attention Deficity Hyperactivity Disorder and Autism

  1. I wastold by Eric Courchesne that I had a normal-sized corpus callosum, but vermal lobes VI-VII in the cerebellum were smaller, not sure if there were hemispheric differences. I wonder if Von Economo neurons could figure into this equaqtion, as I guess they have long connections and travel to disparate parts of the brain and rely on fast transmission of data. I believe they’re found in the prefontal cortex-an area implicated in autism. I think the data is mixed, some studies finding nothing wrong with VE neurons in autism but other researchers, such as Allman at Cal Tech finding VE abnormalities in autism. Of course Eric Courchesne claimed that you were born with the full complement of neurons in the prefrontal area. But, I recall reading, VE neurons can develop in individuals up to age 4. So perhaps, I’m confused. I’ve wondered if I have too many or too few VE neurons and if that could explain in part the etiology of my difficulties.

  2. Hi,

    Thank you for the comments. Inr egards to von Economo neurons, I have previously written the following: Simms, Kemper, Timbie, Bauman, and Blatt (2009) took sections through the anterior cingulate gyrus to perform a preliminary study on the density and size of von Economo neurons (VEN). The study revealed no between group differences for any of the parameters studied. Despite the lack of significance differences the authors undertook post hoc analysis to define subgroups of autistic patients: three brains with increased and six cases with reduced VEN density. The authors believe that their results reflect at a larger scale the heterogeneity in clinical presentation of autistic individuals. However, it may be questionable to provide for data dredging when between groups comparisons were non-significant. Other studies on von Economo neurons have provided unremarkable results. Allman, Watson, Tetreault, and Hakeem (2005) did a preliminary study in two brains of autistic patients noting a large concentration of these cells in the white matter that extended into layers VI and V. Results from an independent group showed no significant differences in VEN (spindle cell) density within the frontoinsular cortex of autistic subjects as compared to controls (Kennedy, Semendeferi, and Courchesne, 2007). The study was the first to offer quantitative stereological data on spindle cell number in autism.
    A significant higher ratio of VEN to pyramidal cells in autistics as compared to controls was reported by Santos et al. (2011). In this study cells were quantitated using stereology in the frontoinsular cortex of 4 autistic children (mean age 7 years, 2 males) and 3 controls (mean age 8.7 years, 2 males). Brains were divided in the midsagittal plane with only one hemisphere available for study. Average section thickness for the autistic tissue sections was 200 μm. Controls were variously quantitated in 200 μm and 500 μm sections with an average thickness of 300 μm for the sections examined. The total number of pyramidal neurons did not differ significantly between groups; however, the patients with autism had significantly higher ratio of VEN to pyramidal cells (3.53 ± 0.55 mean ± s.d.) than controls (2.30 ± 0.34; p = 0.020). Santos et al. (2011) study differed from previous ones in having a higher number of subjects under the age of 16 years (1 subject in Simms et al, 2009, and 2 subjects in Kennedy et al., 2007). It may be the case that pooling together the results of subjects with widely different age ranges may have diluted the effects of younger patients. However, it is difficult to draw meaningful conclusions from the small samples in the aforementioned studies and the absence of precise matching for age, sex, and hemisphere.

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  9. Interesting ideas! So do you think the current perceived comorbidity between autism and ADHD is perhaps a misdiagnosis of a similar “not-paying-attention” phenotype? My son has been diagnosed with both Asperger Syndrome and ADHD, and I have always seen elements of both in him. However his Asperger symptoms are mild and more closely related to social functioning and emotional behaviour (no obsessions etc).

    Thanks for this blog by the way; I am a medical student, parent of 2 sons with developmental disabilities and have a undergrad degree in neuroscience, and am appreciating reading the details of your fascinating research and theories.

  10. I believe that ASD and ADHD share the same expression of symptoms but are not necessarily caused by the same condition. Defining comorbidity is a major problem when diagnostic criteria are based on behaviors. I think that once we move away from a diagnostic classification based on perception to one based on scientific arguments the differences between the conditions (e.g., brain size, white matter connectivity pattern, size of corpus callosum, electrophysiology) will prevail. This is not a trivial thing as it will influence, among other things, the way we treat patients.
    Thank you for your comment about the blog, I try my best to keep terminology down and emphasize ideas. Given time commitments this is very difficult for me. Wish I could do a better job in communicating and disseminating them.

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