Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.
Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.
复制标题
DOI：
10.3389/fncir.2022.681544
复制
发表时间：
2022
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
--
作者：
静息态功能磁共振成像（fMRI）表现出随时间变化的功能连接模式。已经开发了几种不同的分析方法来研究这些静态动力学，包括滑动窗口连接（SWC），相位同步（PS），共激活模式（CAP）和准周期模式（QPP）。每一种办法都可用来形成不同时期的活动模式或区域间协调。然后可以对各个帧进行聚类以产生通常被称为“大脑状态”的时间分组。最近的几篇出版物研究了临床人群中的大脑状态改变，通常使用单一方法来量化逐帧功能连接。本研究直接比较了k均值聚类的结果与三种静息态动力学方法（SWC，CAP和PS），并使用来自人类连接体项目的高分辨率数据量化了几个指标的大脑状态动态。此外，这三种动力学方法进行了比较，通过研究如何大脑状态表征不同的大脑状态的重复序列中确定的第四个动态分析方法，QPP。结果表明，SWC、PS和CAP方法产生的簇和轨迹不同。这些差异的一个清楚的说明是由QPP算法明确识别的24s序列的每个结果如何在一个非常不同的聚类配置文件。PS聚类对QPP敏感，其中大多数QPP序列的中点被分组到相同的单个聚类中。CAP也对QPP高度敏感，将QPP序列的每个相位分成不同的簇集。SWC（60 s窗口）对QPP的敏感性较低。虽然QPP在特定的SWC集群中发生的可能性稍高，但SWC集群在24 s QPP序列中没有变化，这项工作的目标是提高对不同静息态动力学方法的实践和理论理解，从而使研究人员能够更好地概念化和实施这些工具来表征功能性脑网络。
Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.
登录
查看更多内容
静止状态fMRI中的最新进展和运动校正问题。
影响因子：
5.7
作者：
Power JD;Schlaggar BL;Petersen SE
通讯作者：
Petersen SE
影响因子：
4.3
作者：
Keilholz S;Maltbie E;Zhang X;Yousefi B;Pan WJ;Xu N;Nezafati M;LaGrow TJ;Guo Y
通讯作者：
Guo Y
影响因子：
4.2
作者：
Abbas, Anzar;Bassil, Yasmine;Keilholz, Shella
通讯作者：
Keilholz, Shella
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
3.4
作者：
Glerean, Enrico;Salmi, Juha;Sams, Mikko
通讯作者：
Sams, Mikko