Zhou Yueying, Qiao Lishan, Li Weikai, Zhang Limei, Shen Dinggang
School of Mathematics, Liaocheng University, Liaocheng, China.
College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China.
Front Neuroinform. 2018 Feb 6;12:3. doi: 10.3389/fninf.2018.00003. eCollection 2018.
Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FC may contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method.
功能连接（FC）网络已成为理解大脑工作机制和挖掘用于神经/精神疾病诊断的敏感生物标志物的越来越有用的工具。目前，皮尔逊相关性（PC）是FC估计中最简单且最常用的方法。尽管其实证有效性，但PC仅通过计算网络节点（脑区）之间的成对相关性来编码低阶（即二阶）统计量，这无法捕捉FC中涉及的高阶信息（例如，网络中不同边之间的相关性）。为了解决这个问题，我们提出了一种基于矩阵变量正态分布（MVND）的新型FC估计方法，该方法可以同时捕捉低阶和高阶相关性，并且具有清晰的数学可解释性。具体来说，我们首先通过滑动窗口方案生成一组BOLD子序列，并且对于每个子序列，我们通过PC构建一个时间FC网络。然后，我们将构建的FC网络用作样本，通过最大化MVND的似然性来估计最终的低阶和高阶FC网络。为了说明所提出方法的有效性，我们进行实验以从正常对照（NC）中识别轻度认知障碍（MCI）患者。实验结果表明，低阶和高阶FC的融合通常有助于提高最终的分类性能，尽管高阶FC可能比其低阶对应物包含的判别信息更少。重要的是，所提出的同时估计低阶和高阶FC的方法可以比两种基线方法，即原始PC方法和最近的高阶FC估计方法，实现更好的分类性能。
