Zhang Jing, Posse Stefan, Tatsuoka Curtis
Department of Population and Quantitative Health Science, Case Western Reserve University, OH, United States.
Department of Neurosurgery, University of New Mexico, NM, United States.
bioRxiv. 2024 Jun 22:2024.06.18.599636. doi: 10.1101/2024.06.18.599636.
Functional connectivity (FC) is the degree of synchrony of time series between distinct, spatially separated brain regions. While traditional FC analysis assumes the temporal stationarity throughout a brain scan, there is growing recognition that connectivity can change over time and is not stationary, leading to the concept of dynamic FC (dFC). Resting-state functional magnetic resonance imaging (fMRI) can assess dFC using the sliding window method with the correlation analysis of fMRI signals. Accurate statistical inference of sliding window correlation must consider the autocorrelated nature of the time series. Currently, the dynamic consideration is mainly confined to the point estimation of sliding window correlations. Using in vivo resting-state fMRI data, we first demonstrate the non-stationarity in both the cross-correlation function (XCF) and the autocorrelation function (ACF). Then, we propose the variance estimation of the sliding window correlation considering the nonstationary of XCF and ACF. This approach provides a means to dynamically estimate confidence intervals in assessing dynamic connectivity. Using simulations, we compare the performance of the proposed method with other methods, showing the impact of dynamic ACF and XCF on connectivity inference. Accurate variance estimation can help in addressing the critical issue of false positivity and negativity.
功能连接性(FC)是指不同的、空间上分离的脑区之间时间序列的同步程度。虽然传统的FC分析假设在整个脑部扫描过程中时间平稳性,但人们越来越认识到连接性会随时间变化且并非平稳,从而引出了动态FC(dFC)的概念。静息态功能磁共振成像(fMRI)可以使用滑动窗口方法结合fMRI信号的相关性分析来评估dFC。滑动窗口相关性的准确统计推断必须考虑时间序列的自相关性质。目前,动态考虑主要局限于滑动窗口相关性的点估计。利用活体静息态fMRI数据,我们首先证明了互相关函数(XCF)和自相关函数(ACF)中的非平稳性。然后,我们提出了考虑XCF和ACF非平稳性的滑动窗口相关性的方差估计。这种方法提供了一种在评估动态连接性时动态估计置信区间的手段。通过模拟,我们将所提出的方法与其他方法的性能进行了比较,展示了动态ACF和XCF对连接性推断的影响。准确的方差估计有助于解决假阳性和假阴性的关键问题。