Assessing uncertainty in dynamic functional connectivity.
Assessing uncertainty in dynamic functional connectivity.
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DOI：
10.1016/j.neuroimage.2017.01.056
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发表时间：
2017-04-01
期刊：
影响因子：
5.7
通讯作者：
Lindquist MA
中科院分区：
文献类型：
--
作者：
Kudela M;Harezlak J;Lindquist MA
功能连通性（FC）研究解剖学上不同区域的时间序列之间的统计关联，已成为静息状态功能磁共振成像（rs-fMRI）领域的主要研究领域之一。尽管多年来研究人员一直含蓄地认为，在rs-fMRI中，FC在时间上是稳定的，但最近越来越清楚的是，事实并非如此，评估FC动态变化的能力对于更好地理解人类大脑的内部运作至关重要。目前，估计这些动态变化最常用的策略是使用滑动窗口技术。然而，它最大的缺点是估计中存在固有的变化，即使对于null数据也是如此，这很容易与连接中真正随时间变化的变化相混淆。这可能会产生严重的后果，因为即使是由噪声引起的虚假波动也很容易与重要信号混淆。由于这些原因，评估滑动窗口相关估计中的不确定性至关重要。本文提出了一种结合多元线性过程自举（MLPB）方法和滑动窗口技术的新方法，通过提供动态FC估计的置信带来评估其不确定性。最后给出了数值结果和在rs-fMRI研究中的应用，证明了该方法的有效性。
Functional connectivity (FC) – the study of the statistical association between time series from anatomically distinct regions – has become one of the primary areas of research in the field surrounding resting state functional magnetic resonance imaging (rs-fMRI). Although for many years researchers have implicitly assumed that FC was stationary across time in rs-fMRI, it has recently become increasingly clear that this is not the case and the ability to assess dynamic changes in FC is critical for better understanding of the inner workings of the human brain. Currently, the most common strategy for estimating these dynamic changes is to use the sliding-window technique. However, its greatest shortcoming is the inherent variation present in the estimate, even for null data, which is easily confused with true time-varying changes in connectivity. This can have serious consequences as even spurious fluctuations caused by noise can easily be confused with an important signal. For these reasons, assessment of uncertainty in the sliding-window correlation estimates is of critical importance. Here we propose a new approach that combines the multivariate linear process bootstrap (MLPB) method and a sliding-window technique to assess the uncertainty in a dynamic FC estimate by providing its confidence bands. Both numerical results and an application to rs-fMRI study are presented, showing the efficacy of the proposed method.