Objective . Statistical methods that simultaneously model temporal and spatial variations of fMRI data are promising tools for dynamic functional connectivity (FC) estimation. Although different approaches are available, they need to manually set the parameters, or may disregard some important fMRI features such as the autocorrelation. In addition, no reliable method exists for the validation of dynamic FC analysis models. Approach . In the present study, we have proposed an autoregressive dynamic conditional correlation model to deal with the temporal autocorrelation and non-stationarity in fMRI time-series. This model assumes that the brain time courses follow a multivariate Gaussian distribution, and that the conditional mean, variance and covariances change in an autoregressive form. Also, we proposed a new measurement index for the evaluation of the statistical consistency between the inferred dynamic functional connectivity and the real fMRI data. The performa...

中文翻译：

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使用带有残差分析的自回归动态条件相关模型提取动态功能连通性。

目标。同时建模fMRI数据的时空变化的统计方法是用于动态功能连接（FC）估计的有前途的工具。尽管可以使用不同的方法，但是它们需要手动设置参数，或者可能忽略一些重要的功能磁共振成像功能，例如自相关。此外，不存在用于验证动态FC分析模型的可靠方法。方法。在本研究中，我们提出了一种自回归动态条件相关模型来处理fMRI时间序列中的时间自相关和非平稳性。该模型假设脑时间过程遵循多元高斯分布，并且条件均值，方差和协方差以自回归形式变化。也，我们提出了一种新的测量指标，用于评估推断的动态功能连接性与实际fMRI数据之间的统计一致性。表演...