Palande Sourabh, Jose Vipin, Zielinski Brandon, Anderson Jeffrey, Fletcher P Thomas, Wang Bei
Scientific Computing and Imaging Institute, University of Utah.
School of Computing, University of Utah.
Connectomics Neuroimaging (2017). 2017 Sep;10511:98-107. doi: 10.1007/978-3-319-67159-8_12. Epub 2017 Sep 2.
A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance MRI (scMRI) is a technique that maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender- and IQ-matched controls. Specifically, we investigate topological differences in gray matter structures captured by structural covariance networks (SCNs) derived from three ICNs strongly implicated in autism, namely, the salience network (SN), the default mode network (DMN) and the executive control network (ECN). By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using scMRI (Zielinski et al. 2012).
大量证据表明自闭症与大脑结构和功能连接异常有关。结构协方差磁共振成像（scMRI）是一种在不同受试者间绘制灰质密度协变脑区的技术。它通过分析灰质信号协方差，提供了一种探究内在连接网络（ICN）潜在解剖结构的方法。在本文中，我们将拓扑数据分析与scMRI相结合，以探索自闭症患者与年龄、性别和智商匹配的对照组在灰质结构上的网络特异性差异。具体而言，我们研究了从三个与自闭症密切相关的ICN衍生出的结构协方差网络（SCN）所捕获的灰质结构的拓扑差异，这三个ICN分别是突显网络（SN）、默认模式网络（DMN）和执行控制网络（ECN）。通过将拓扑数据分析与统计推断相结合，我们的结果提供了证据，表明来自SN和ECN的SCN在自闭症中存在具有统计学意义的网络特异性结构异常。这些大脑结构上的差异与使用scMRI进行的直接结构分析结果一致（Zielinski等人，2012年）。
