Journal
ADVANCES IN MEDICAL SCIENCES
Volume 56, Issue 2, Pages 334-342Publisher
MEDICAL UNIV BIALYSTOK
DOI: 10.2478/v10039-011-0042-y
Keywords
Autism spectrum disorder; Asperger syndrome; high-functioning autism; single-nucleotide polymorphisms; brain morphometry; diagnostic model
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Funding
- China Scholarship Council [2008101370]
- National Natural Science foundation of China [30570655]
- Scientific Research Foundation of Graduate School of Southeast University [YBJJ1011]
- National Institutes of Health [R01 AG13743, R03 EB009310]
- National Institute of Aging
- National Institute of Mental Health
- National Cancer Institute
- Natural Science Foundation of Jiangsu, China [BK2008082]
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Purpose: Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Material and Methods: Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. Results: For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Conclusion: Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP-rs878960 in GABRB3-distinguishes Asperger syndrome from high-functioning autism.
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