Multivariate characterization of white matter heterogeneity in autism spectrum disorder.
Multivariate characterization of white matter heterogeneity in autism spectrum disorder.
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自闭症谱系障碍中白质异质性的多元表征。
DOI：
10.1016/j.nicl.2017.01.002
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Alexander AL
中科院分区：
文献类型：
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作者：
Dean DC 3rd;Lange N;Travers BG;Prigge MB;Matsunami N;Kellett KA;Freeman A;Kane KL;Adluru N;Tromp DP;Destiche DJ;Samsin D;Zielinski BA;Fletcher PT;Anderson JS;Froehlich AL;Leppert MF;Bigler ED;Lainhart JE;Alexander AL
自闭症谱系障碍患者神经影像学发现的复杂性和异质性表明，许多潜在的改变是微妙的，涉及许多大脑区域和网络。因此，解释多变量大脑特征和识别可用于表征个体差异的神经成像测量的能力对于解释和理解自闭症的神经生物学机制变得越来越重要。在本研究中，我们利用马氏距离（欧几里得距离的多维对应物）作为信息指标来表征自闭症个体大脑变异和偏差。在大约10年的时间里，研究人员获得了年龄在3.1至36.83岁之间的149名参与者（92名被诊断为自闭症谱系障碍，57名正常发展的对照组）的纵向扩散张量成像数据，并利用白质微观结构的区域测量来构建马氏距离。与对照组相比，自闭症个体的马氏距离明显更大，变化更大，这表明自闭症谱系障碍个体组的非典型性和变异性增加。研究还发现，与传统的单变量测量相比，多变量测量的分布在自闭症和正常发育个体之间提供了更大的区别和更敏感的描述，同时也与观察到的自闭症群体特征显著相关。这些结果有助于证实自闭症是一种真正的异质性神经发育障碍，同时也表明，综合考虑来自多个大脑区域的神经成像测量，可以更好地了解自闭症大脑测量的多样性，而单独考虑相同区域时无法观察到这一点。因此，区分多维脑关系可能有助于识别基于神经成像的表型，并有助于阐明自闭症谱系障碍中大脑变异的潜在神经机制。马氏距离被提议从一组多变量的神经成像测量来表征大脑差异。使用马氏距离对潜在的微观结构敏感来检查自闭症的大脑变异。比较了马氏距离与传统的单变量测量方法。介绍了多元脑关系的实用性与马氏距离的关系以及未来研究的前景。
The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders. Mahalanobis distance proposed to characterize brain differences from a multivariate set of neuroimaging measures. Brain variation in autism is examined using Mahalanobis distance is sensitive to underlying microstructure. Comparison of Mahalanobis distance with traditional univariate measures is examined. Utility of multivariate brain relations with Mahalanobis distance and prospect of future studies are described.