| Title |
Spherical harmonic analysis of cortical complexity in autism and dyslexia
|
|---|---|
| Published in |
Translational Neuroscience, March 2012
|
| DOI | 10.2478/s13380-012-0008-y |
| Pubmed ID | |
| Authors |
Emily Williams, Ayman El-Baz, Matthew Nitzken, Andrew Switala, Manuel Casanova |
| Abstract |
Alterations in gyral form and complexity have been consistently noted in both autism and dyslexia. In this present study, we apply spherical harmonics, an established technique which we have exapted to estimate surface complexity of the brain, in order to identify abnormalities in gyrification between autistics, dyslexics, and controls. On the order of absolute surface complexity, autism exhibits the most extreme phenotype, controls occupy the intermediate ranges, and dyslexics exhibit lesser surface complexity. Here, we synthesize our findings which demarcate these three groups and review how factors controlling neocortical proliferation and neuronal migration may lead to these distinctive phenotypes. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Netherlands | 1 | 3% |
| Unknown | 37 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 23% |
| Student > Ph. D. Student | 8 | 21% |
| Lecturer | 3 | 8% |
| Student > Bachelor | 3 | 8% |
| Professor | 3 | 8% |
| Other | 3 | 8% |
| Unknown | 10 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 6 | 15% |
| Medicine and Dentistry | 5 | 13% |
| Agricultural and Biological Sciences | 4 | 10% |
| Psychology | 4 | 10% |
| Engineering | 3 | 8% |
| Other | 6 | 15% |
| Unknown | 11 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 November 2015.
All research outputs
#4,369,297
of 25,371,288 outputs
Outputs from Translational Neuroscience
#31
of 199 outputs
Outputs of similar age
#27,123
of 168,987 outputs
Outputs of similar age from Translational Neuroscience
#1
of 3 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 199 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one has done well, scoring higher than 84% of its peers.
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We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them