Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging.
Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging.
复制标题
fMRI 和 MEG 成像中动态静息态功能连接的时变动态网络模型。
DOI：
10.1016/j.neuroimage.2022.119131
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发表时间：
2022-07-01
期刊：
影响因子：
5.7
通讯作者：
Nagarajan, Srikantan
中科院分区：
文献类型：
--
作者：
Jiang, Fei;Jin, Huaqing;Gao, Yijing;Xie, Xihe;Cummings, Jennifer;Raj, Ashish;Nagarajan, Srikantan
关键词：
动态静息状态功能连接（RSFC）表征了功能性脑网络中随时间发生的波动。现有的方法来提取动态RSFC，如滑动窗口和聚类方法，本质上是非自适应的，具有各种限制，如高维，无法重建大脑信号，可靠的估计数据不足，不敏感的快速变化的动态，以及缺乏跨多功能成像模态的概括性。为了克服这些不足，我们开发了一个新的和统一的时变动态网络（TVDN）的框架，检查动态静息状态的功能连接。TVDN包括描述低维动态RSFC与脑信号之间的关系的生成模型，以及自动且自适应地学习动态RSFC的低维流形并检测数据中的动态状态转换的推理算法。TVDN适用于多种功能性神经成像模式，如fMRI和MEG/EEG。估计的低维动态RSFCs流形直接链接到大脑信号的频率内容。因此，我们可以通过检查学习的特征是否可以重建观察到的大脑信号来评估TVDN的性能。我们进行全面的模拟，以评估TVDN在假设的设置。然后，我们展示了TVDN与真实的功能磁共振成像和脑磁图数据的应用，并与现有的基准比较的结果。结果表明，TVDN是能够正确地捕捉大脑活动的动力学和更鲁棒地检测大脑状态切换在静息态fMRI和MEG数据。
Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.
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影响因子：
5.7
作者：
Abdelnour F;Dayan M;Devinsky O;Thesen T;Raj A
通讯作者：
Raj A
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
3
作者：
Brunton, Bingni W.;Johnson, Lise A.;Kutz, J. Nathan
通讯作者：
Kutz, J. Nathan
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
DOI：
10.1073/pnas.1018985108
发表时间：
2011-05-03
影响因子：
11.1
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
通讯作者：
Grafton, Scott T.