Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity.
Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity.
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DOI：
10.1371/journal.pone.0190220
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Stern JM
中科院分区：
文献类型：
--
作者：
Chiang S;Vankov ER;Yeh HJ;Guindani M;Vannucci M;Haneef Z;Stern JM
功能连通性(Function Connection，FC)估计已成为研究健康和异常脑功能的一种日益强大的工具。尤其是静态连通性，在指导大多数静息状态功能磁共振研究的结论方面发挥了很大作用。然而，越来越多的证据表明，FC存在时间波动，这导致人们对将FC作为一个动态量进行估计的兴趣越来越大。在这种新的连通性观点中出现的一个中心问题是，动态功能连通性(DFC)估计导致复杂性急剧增加。为了在计算上处理这种增加的复杂性，通常考虑有限的DFC属性集，主要是均值和方差。此外，如何将DFC中增加的信息整合到模式识别技术中以进行主题级预测仍不清楚。在这项研究中，我们提出了一种基于大量以前未被探索的动态功能连接的时间和频谱特征的方法来解决这两个问题。利用广义自回归条件异方差(GARCH)模型估计静止态网络之间功能连通性的时变模式。然后对DFC估计进行时频分析，并从信号处理文献中提取大量先前未探索的时间和频谱特征用于DFC估计。我们将研究的特征应用于两个感兴趣的神经学人群，健康对照组和颞叶癫痫患者，并表明与传统的静态连通性估计和当前的DFC方法相比，所提出的方法在预测性能上都有显著的提高。变量重要性的评估表明，可以从DFC信号中提取几个量，这些量比传统的DFC均值或方差更具信息量。这项工作阐明了静息状态网络之间功能连通性的动态特性的许多以前未被探索的方面，并为动态功能连通性分析提供了一个平台，有助于将其用作健康和异常大脑功能的研究指标。
Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
3.4
作者：
Constable RT;Scheinost D;Finn ES;Shen X;Hampson M;Winstanley FS;Spencer DD;Papademetris X
通讯作者：
Papademetris X
图理论指标在功能连通性分析中的时间依赖性。
影响因子：
5.7
作者：
Chiang S;Cassese A;Guindani M;Vannucci M;Yeh HJ;Haneef Z;Stern JM
通讯作者：
Stern JM
影响因子：
2.9
作者：
Boubela RN;Kalcher K;Huf W;Kronnerwetter C;Filzmoser P;Moser E
通讯作者：
Moser E
影响因子：
4.3
作者：
Gonzalez-Castillo J;Handwerker DA;Robinson ME;Hoy CW;Buchanan LC;Saad ZS;Bandettini PA
通讯作者：
Bandettini PA