Title |
Facial Structure Analysis Separates Autism Spectrum Disorders into Meaningful Clinical Subgroups
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Published in |
Journal of Autism and Developmental Disorders, October 2014
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DOI | 10.1007/s10803-014-2290-8 |
Pubmed ID | |
Authors |
Tayo Obafemi-Ajayi, Judith H. Miles, T. Nicole Takahashi, Wenchuan Qi, Kristina Aldridge, Minqi Zhang, Shi-Qing Xin, Ying He, Ye Duan |
Abstract |
Varied cluster analysis were applied to facial surface measurements from 62 prepubertal boys with essential autism to determine whether facial morphology constitutes viable biomarker for delineation of discrete Autism Spectrum Disorders (ASD) subgroups. Earlier study indicated utility of facial morphology for autism subgrouping (Aldridge et al. in Mol Autism 2(1):15, 2011). Geodesic distances between standardized facial landmarks were measured from three-dimensional stereo-photogrammetric images. Subjects were evaluated for autism-related symptoms, neurologic, cognitive, familial, and phenotypic variants. The most compact cluster is clinically characterized by severe ASD, significant cognitive impairment and language regression. This verifies utility of facially-based ASD subtypes and validates Aldridge et al.'s severe ASD subgroup, notwithstanding different techniques. It suggests that language regression may define a unique ASD subgroup with potential etiologic differences. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 4 | 27% |
Switzerland | 1 | 7% |
Canada | 1 | 7% |
United Kingdom | 1 | 7% |
Unknown | 8 | 53% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 11 | 73% |
Scientists | 3 | 20% |
Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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South Africa | 1 | 1% |
Unknown | 92 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 16 | 17% |
Student > Ph. D. Student | 14 | 15% |
Researcher | 9 | 10% |
Student > Bachelor | 8 | 9% |
Student > Doctoral Student | 4 | 4% |
Other | 14 | 15% |
Unknown | 28 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 15 | 16% |
Psychology | 15 | 16% |
Computer Science | 6 | 6% |
Social Sciences | 5 | 5% |
Neuroscience | 5 | 5% |
Other | 18 | 19% |
Unknown | 29 | 31% |