کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6036582 1188777 2010 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predictive models of autism spectrum disorder based on brain regional cortical thickness
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
پیش نمایش صفحه اول مقاله
Predictive models of autism spectrum disorder based on brain regional cortical thickness
چکیده انگلیسی
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a wide phenotypic range, often affecting personality and communication. Previous voxel-based morphometry (VBM) studies of ASD have identified both gray- and white-matter volume changes. However, the cerebral cortex is a 2-D sheet with a highly folded and curved geometry, which VBM cannot directly measure. Surface-based morphometry (SBM) has the advantage of being able to measure cortical surface features, such as thickness. The goals of this study were twofold: to construct diagnostic models for ASD, based on regional thickness measurements extracted from SBM, and to compare these models to diagnostic models based on volumetric morphometry. Our study included 22 subjects with ASD (mean age 9.2 ± 2.1 years) and 16 volunteer controls (mean age 10.0 ± 1.9 years). Using SBM, we obtained regional cortical thicknesses for 66 brain structures for each subject. In addition, we obtained volumes for the same 66 structures for these subjects. To generate diagnostic models, we employed four machine-learning techniques: support vector machines (SVMs), multilayer perceptrons (MLPs), functional trees (FTs), and logistic model trees (LMTs). We found that thickness-based diagnostic models were superior to those based on regional volumes. For thickness-based classification, LMT achieved the best classification performance, with accuracy = 87%, area under the receiver operating characteristic (ROC) curve (AUC) = 0.93, sensitivity = 95%, and specificity = 75%. For volume-based classification, LMT achieved the highest accuracy, with accuracy = 74%, AUC = 0.77, sensitivity = 77%, and specificity = 69%. The thickness-based diagnostic model generated by LMT included 7 structures. Relative to controls, children with ASD had decreased cortical thickness in the left and right pars triangularis, left medial orbitofrontal gyrus, left parahippocampal gyrus, and left frontal pole, and increased cortical thickness in the left caudal anterior cingulate and left precuneus. Overall, thickness-based classification outperformed volume-based classification across a variety of classification methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 50, Issue 2, 1 April 2010, Pages 589-599
نویسندگان
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