Describing the Brain in Autism in Five Dimensions-Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach
Describing the Brain in Autism in Five Dimensions-Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach
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DOI：
10.1523/jneurosci.5413-09.2010
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发表时间：
2010-08-11
影响因子：
5.3
通讯作者：
Murphy, Declan G. M.
中科院分区：
文献类型：
--
作者：
Ecker, Christine;Marquand, Andre;Murphy, Declan G. M.
自闭症谱系障碍（ASD）是一种神经发育状况，具有多种原因，共病条件，以及由不同个体表达的广泛的症状类型和严重程度。这使得自闭症的神经解剖学本质上难以描述。在这里，我们演示了如何多参数分类方法可以用来表征复杂和微妙的结构模式的灰质解剖牵连在成人ASD，并揭示空间分布模式的各种参数描述大脑解剖的区别区域。一组五个形态学参数，包括体积和几何特征，在每个空间位置上的皮质表面被用来区分人与ASD和控制使用支持向量机（SVM）的分析方法，并找到一个空间分布模式的区域与最大的分类权重。在这些模式的基础上，SVM能够以高达90%和80%的灵敏度和特异性识别ASD个体。然而，个体皮质特征区分各组的能力是高度可变的，并且区域的区分模式在参数之间变化。分类是特定于ASD，而不是一般的神经发育条件（e。例如，在一个实施例中，注意力缺陷多动障碍）。我们的研究结果证实了这一假设，即自闭症的神经解剖学是真正多维的，并影响多个和最有可能独立的皮质功能。使用SVM检测到的空间模式可能有助于进一步探索ASD的特定遗传和神经病理学基础，并为该疾病最可能的多因素病因提供新的见解。
Autism spectrum disorder (ASD) is a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. Here, we demonstrate how a multiparameter classification approach can be used to characterize the complex and subtle structural pattern of gray matter anatomy implicated in adults with ASD, and to reveal spatially distributed patterns of discriminating regions for a variety of parameters describing brain anatomy. A set of five morphological parameters including volumetric and geometric features at each spatial location on the cortical surface was used to discriminate between people with ASD and controls using a support vector machine (SVM) analytic approach, and to find a spatially distributed pattern of regions with maximal classification weights. On the basis of these patterns, SVM was able to identify individuals with ASD at a sensitivity and specificity of up to 90% and 80%, respectively. However, the ability of individual cortical features to discriminate between groups was highly variable, and the discriminating patterns of regions varied across parameters. The classification was specific to ASD rather than neurodevelopmental conditions in general (e. g., attention deficit hyperactivity disorder). Our results confirm the hypothesis that the neuroanatomy of autism is truly multidimensional, and affects multiple and most likely independent cortical features. The spatial patterns detected using SVM may help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multifactorial etiology of the condition.