Chiang Sharon, Cassese Alberto, Guindani Michele, Vannucci Marina, Yeh Hsiang J, Haneef Zulfi, Stern John M
Department of Statistics, Rice University, Houston, TX, USA.
Department of Statistics, Rice University, Houston, TX, USA; Department of Biostatistics, University of Texas at MD Anderson Cancer Center, Houston, TX, USA; Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands.
Neuroimage. 2016 Jan 15;125:601-615. doi: 10.1016/j.neuroimage.2015.10.070. Epub 2015 Oct 27.
Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.
脑图谱为以计算方式对连接组的网络结构进行建模提供了一种有用的方法，这使得人们越来越有兴趣使用图论来在网络层面定量研究健康大脑和脑部疾病的拓扑特征。大多数关于功能连接性的图论研究都依赖于时间平稳性的假设。然而，最近的证据越来越表明，功能连接性在扫描过程中会发生波动。在本研究中，我们使用贝叶斯隐马尔可夫模型（HMM）方法来研究脑网络拓扑的平稳性，该方法可估计全脑功能连接性的图论度量的动态结构。除了提取常用图论度量的平稳分布和转移概率外，我们还提出了两个时间平稳性的估计量：S指数和N指数。这些指数可用于量化图论度量时间平稳性的不同方面。我们将该方法和提出的估计量应用于健康对照和颞叶癫痫患者的静息态功能磁共振成像数据。我们的分析表明，包括小世界指数、全局整合度量和介数中心性在内的几个图论度量可能随时间表现出更大的平稳性，因此更稳健。此外，我们证明，考虑网络拓扑时间平稳性水平的个体差异可能会提高区分疾病状态的辨别力。我们的结果证实并扩展了其他关于功能连接性动态性质的研究结果，并表明在图论分析中使用明确考虑功能连接性动态性质的统计模型可能会提高研究的敏感性以及不同研究之间的一致性。
