Chen Rong, Herskovits Edward H
Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA.
Neuroimage. 2006 Feb 15;29(4):1252-9. doi: 10.1016/j.neuroimage.2005.08.020. Epub 2005 Oct 5.
We present a network analysis of a cross-sectional study of mild cognitive impairment (MCI). Network analysis, as opposed to univariate analysis, accounts for interactions among brain structures in explaining a clinical outcome. In this context, we analyze structural magnetic resonance (MR) data based on a Bayesian network representation of variables in the problem domain. The Bayesian network resulting from this analysis reveals complex, nonlinear multivariate associations among morphological changes in the left hippocampus and in the right thalamus and the presence of mild cognitive impairment. This Bayesian network could be used to predict the presence of mild cognitive impairment from structural MR scans.
我们展示了一项针对轻度认知障碍（MCI）横断面研究的网络分析。与单变量分析不同，网络分析在解释临床结果时考虑了脑结构之间的相互作用。在此背景下，我们基于问题域中变量的贝叶斯网络表示来分析结构磁共振（MR）数据。该分析得出的贝叶斯网络揭示了左侧海马体和右侧丘脑形态变化与轻度认知障碍之间复杂的非线性多变量关联。这个贝叶斯网络可用于从结构MR扫描预测轻度认知障碍的存在。
