Predictive models of autism spectrum disorder based on brain regional cortical thickness.
Predictive models of autism spectrum disorder based on brain regional cortical thickness.
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DOI：
10.1016/j.neuroimage.2009.12.047
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发表时间：
2010-04-01
期刊：
影响因子：
5.7
通讯作者：
Herskovits, Edward H.
中科院分区：
文献类型：
--
作者：
Jiao, Yun;Chen, Rong;Ke, Xiaoyan;Chu, Kangkang;Lu, Zuhong;Herskovits, Edward H.
自闭症谱系障碍（ASD）是一种神经发育障碍，具有广泛的表型范围，经常影响人格和沟通。先前的基于体素的ASD形态学（VBM）研究已经确定了灰质和白质体积的变化。然而，大脑皮层是一个具有高度折叠和弯曲几何形状的二维薄片，VBM无法直接测量。基于表面的形态测量（SBM）具有能够测量皮质表面特征（诸如厚度）的优点。本研究的目标是双重的：构建ASD的诊断模型，基于从SBM提取的区域厚度测量值;并将这些模型与基于体积形态测量的诊断模型进行比较。我们的研究包括22名ASD受试者（平均年龄9.2 ± 2.1岁）和16名志愿者对照（平均年龄10.0 ± 1.9岁）。使用SBM，我们获得了66个脑结构的区域皮质厚度为每个主题。此外，我们获得了这些主题的相同66个结构的卷。为了生成诊断模型，我们采用了四种机器学习技术：支持向量机（SVM），多层感知器（MLP），功能树（FT）和逻辑模型树（LMT）。我们发现基于厚度的诊断模型优于基于区域体积的诊断模型，具有上级性。对于基于厚度的分类，LMT实现了最佳分类性能，准确度= 87%，受试者工作特征（ROC）曲线下面积（AUC）= 0.93，灵敏度= 95%，特异性= 75%。对于基于体积的分类，LMT实现了最高的准确性，准确性= 74%，AUC = 0.77，灵敏度= 77%，特异性= 69%。LMT生成的基于厚度的诊断模型包括7个结构。与对照组相比，ASD儿童的左、右三角部、左内侧眶额回、左海马旁回和左额极皮质厚度减少，左尾侧前扣带回和左楔前叶皮质厚度增加。总体而言，基于厚度的分类在各种分类方法中优于基于体积的分类。
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.
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影响因子：
5.7
作者：
Fischl, B;Sereno, MI;Dale, AM
通讯作者：
Dale, AM
影响因子：
3.7
作者：
Dickerson, Bradford C.;Bakkour, Akram;Salat, David H.;Feczko, Eric;Pacheco, Jenni;Greve, Douglas N.;Grodstein, Fran;Wright, Christopher I.;Blacker, Deborah;Rosas, H. Diana;Sperling, Reisa A.;Atri, Alireza;Growdon, John H.;Hyman, Bradley T.;Morris, John C.;Fischl, Bruce;Buckner, Randy L.
通讯作者：
Buckner, Randy L.
DOI：
10.1097/00004583-200403000-00018
发表时间：
2004-03-01
影响因子：
13.3
作者：
Akshoomoff, N;Lord, C;Courchesne, E
通讯作者：
Courchesne, E
影响因子：
9.9
作者：
Aylward, EH;Minshew, NJ;Singh, N
通讯作者：
Singh, N
影响因子：
2.9
作者：
Gray, Jeremy R.;Braver, Todd S.
通讯作者：
Braver, Todd S.