Espinoza Flor A, Vergara Victor M, Damaraju Eswar, Henke Kyle G, Faghiri Ashkan, Turner Jessica A, Belger Aysenil A, Ford Judith M, McEwen Sarah C, Mathalon Daniel H, Mueller Bryon A, Potkin Steven G, Preda Adrian, Vaidya Jatin G, van Erp Theo G M, Calhoun Vince D
Mind Research Network, Albuquerque, NM, United States.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
Front Neurosci. 2019 Jun 27;13:634. doi: 10.3389/fnins.2019.00634. eCollection 2019.
Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.
在静息态功能磁共振成像（fMRI）实验中，大脑功能连接已被证明会随时间变化。对时间变化的仔细研究揭示了一小部分全脑连接模式，称为动态状态。动态功能网络连接（dFNC）研究表明，在多个静息态实验中复制动态状态是可能的。然而，状态及其时间动态性的估计仍然受到噪声和不完美估计的影响。在常规的dFNC实现中，通过比较整个数据中的连接模式来估计状态，而不考虑时间，换句话说，只检查零阶变化。在这项工作中，我们提出了一种方法，该方法在动态连接模式的搜索方案中纳入了dFNC的一阶变化。我们的方法，称为功能网络连接的时间变化（tvFNC），估计dFNC的导数，然后搜索并发dFNC状态及其导数的重复出现模式。tvFNC方法首先使用模拟数据集进行验证，然后应用于一个包括健康对照（HC）和精神分裂症（SZ）患者的静息态fMRI样本，并与标准dFNC方法进行比较。我们的动态方法在连接导数中揭示了额外的模式，补充了已经报道的状态模式。状态导数包含了原始dFNC方法未观察到的关于脑网络之间连接增加和减少的额外信息。通过揭示每个状态下HC组和SZ组之间额外的FNC差异，tvFNC比常规dFNC表现出更高的敏感性。总之，tvFNC方法提供了一种新的、增强的方法来检查随时间变化的功能连接。
