Time-dependence of graph theory metrics in functional connectivity analysis.
Time-dependence of graph theory metrics in functional connectivity analysis.
复制标题
图理论指标在功能连通性分析中的时间依赖性。
DOI：
10.1016/j.neuroimage.2015.10.070
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发表时间：
2016-01-15
期刊：
影响因子：
5.7
通讯作者：
Stern JM
中科院分区：
文献类型：
--
作者：
Chiang S;Cassese A;Guindani M;Vannucci M;Yeh HJ;Haneef Z;Stern JM
脑图提供了一种有用的方法来计算模型的连接体的网络结构，这导致了越来越多的兴趣，在使用图论来量化和调查的拓扑特征的健康的大脑和大脑疾病的网络水平。大多数功能连接的图论研究都依赖于时间平稳性的假设。然而，最近的证据越来越多地表明，功能连接在扫描的长度上波动。在这项研究中，我们调查的平稳性的大脑网络拓扑结构使用贝叶斯隐马尔可夫模型（HMM）的方法，估计全脑功能连接的图论措施的动态结构。除了提取常用的图论措施的平稳分布和转移概率，我们提出了两个估计的时间平稳性：S-指数和N-指数。这些指标可以用来量化不同方面的时间平稳性的图论措施。我们应用的方法和建议的估计，从健康对照组和颞叶癫痫患者的静息状态功能磁共振成像数据。我们的分析表明，几个图论的措施，包括小世界指数，全球一体化措施，介数中心，可能会表现出更大的平稳性随着时间的推移，因此更强大。此外，我们表明，占受试者水平的网络拓扑结构的时间平稳性水平的差异，可能会增加区分疾病状态之间的歧视权力。我们的研究结果证实和扩展的结果从其他研究的动态性质的功能连接，并建议使用统计模型，明确说明的动态性质的功能连接的图论分析可以提高调查的灵敏度和一致性的调查。
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.