A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks.

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks.
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DOI:
10.1162/netn_a_00155
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发表时间:
2021
期刊:
Network neuroscience (Cambridge, Mass.)
影响因子:
--
通讯作者:
Calhoun VD
Calhoun VD
中科院分区:
其他
文献类型:
--
作者:
Faghiri A;Iraji A;Damaraju E;Turner J;Calhoun VD

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静态和动态功能网络连通性(FNC)通常被单独研究,这使得我们无法在每个分析中看到完整的连通性谱。在这里,我们提出了一种称为滤波器组连接(FBC)的方法来估计连接,同时保留其完整的频率范围,并随后在一个统一的方法中检查静态和动态连接。首先,我们证明了FBC可以估计滑动窗口方法错过的多个频率的连接。接下来,我们使用FBC估计静息状态fMRI数据集,包括精神分裂症患者(SZ)和典型的控制(TC)的FNC。FBC结果被聚集到不同的网络状态中。一些状态显示出弱的低频强度,因此在基于窗口的方法中没有捕获。此外,我们发现,与在较低频率状态下花费更多时间的TC相比,SZ倾向于在表现出较高频率的状态下花费更多时间。最后,我们表明,FBC使我们能够分析静态和动态连接在一个统一的方式。总之,FBC提供了一种统一静态和动态连接分析的新方法,并可以提供有关连接模式频率分布的其他信息。
Static and dynamic functional network connectivity (FNC) are typically studied separately, which makes us unable to see the full spectrum of connectivity in each analysis. Here, we propose an approach called filter-banked connectivity (FBC) to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. First, we demonstrate that FBC can estimate connectivity across multiple frequencies missed by a sliding-window approach. Next, we use FBC to estimate FNC in a resting-state fMRI dataset including schizophrenia patients (SZ) and typical controls (TC). The FBC results are clustered into different network states. Some states showed weak low-frequency strength and as such were not captured in the window-based approach. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies compared with TCs who spent more time in lower frequency states. Finally, we show that FBC enables us to analyze static and dynamic connectivity in a unified way. In summary, FBC offers a novel way to unify static and dynamic connectivity analyses and can provide additional information about the frequency profile of connectivity patterns.
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