Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference.
Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference.
复制标题
使用拓扑启发的统计推断重新审视自闭症背后的大脑网络架构的异常。
DOI：
10.1089/brain.2018.0604
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发表时间：
2019
影响因子：
3.4
通讯作者：
Wang,Bei
中科院分区：
文献类型：
--
作者：
Palande,Sourabh;Jose,Vipin;Zielinski,Brandon;Anderson,Jeffrey;Fletcher,PThomas;Wang,Bei
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）是一种技术，绘制大脑区域的共变灰质密度横跨受试者。它提供了一种通过分析灰质信号协方差来探索内在连接网络（ICNs）的解剖结构的方法。在这篇文章中，我们将拓扑数据分析与scMRI相结合，探索自闭症受试者与年龄、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。具体来说，我们研究了由三个与自闭症密切相关的ICNs（即显著性网络、默认模式网络和执行控制网络）的结构相关图所捕获的灰质结构的拓扑差异。通过将拓扑数据分析与统计推断相结合，我们的结果为自闭症中具有统计意义的网络特异性结构异常提供了证据。
A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance magnetic resonance imaging (scMRI) is a technique that maps brain regions with covarying gray matter densities across subjects. It provides a way to probe the anatomical structure underlying intrinsic connectivity networks (ICNs) through analysis of gray matter signal covariance. In this article, 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 structure captured by structural correlation graphs derived from three ICNs strongly implicated in autism, namely the salience network, default mode network, and executive control network. By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism.