Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P Thomas Fletcher, Bei Wang
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引用次数: 22
Abstract
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)是一种技术,绘制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测内在连接网络(ICNs)的解剖结构的方法。在本文中,我们将拓扑数据分析与scMRI相结合,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icn(即显著性网络(SN)、默认模式网络(DMN)和执行控制网络(ECN)衍生的结构协方差网络(scn)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,我们的研究结果从SN和ECN衍生的scn中提供了自闭症中具有统计学意义的网络特异性结构异常的证据。大脑结构的这些差异与使用scMRI进行的直接结构分析是一致的(Zielinski et al. 2012)。