Lee David, Donald Kirsten A, Dalal Taykhoom, Wedderburn Catherine J, Roos Annerine, Ipser Jonathan, Subramoney Sivenesi, Zar Heather J, Stein Dan J, Narr Katherine L, Hellemann Gerhard, Woods Roger P, Joshi Shantanu H
Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, USA.
Department of Bioengineering, UCLA USA.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:995-998. doi: 10.1109/isbi45749.2020.9098689. Epub 2020 May 22.
We present a new method for constructing structural inference brain networks from functional measures of cortical features. Instead of averaging vertex-wise cortical features, we propose the use of full functions of spatial densities of measures such as thickness and use two dimensional pairwise correlations between regions to construct population networks. We show increased within group correlations for both healthy controls and toddlers with prenatal alcohol exposure compared to the existing mean-based correlation approach. Further, we also show significant differences in brain connectivity between the healthy controls and the exposed group.
我们提出了一种从皮质特征的功能测量构建结构推理脑网络的新方法。我们不是对顶点层面的皮质特征进行平均，而是建议使用诸如厚度等测量的空间密度的完整函数，并利用区域之间的二维成对相关性来构建群体网络。与现有的基于均值的相关性方法相比，我们发现健康对照组和产前酒精暴露的幼儿组内相关性均有所增加。此外，我们还显示出健康对照组和暴露组之间脑连接性存在显著差异。
