Sliding window functional connectivity inference with nonstationary autocorrelations and cross-correlations

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. HighlightsO_LIWe study the impact of nonstationary covariance on inference in resting-state fMRI C_LIO_LIWe propose dual sliding window estimators for correlation and its variance C_LIO_LIDynamic Z scores and confidence intervals characterize fluctuations of connectivity C_LIO_LIA statistical manner to determine static vs. dynamic connectivity at the inter-regional level C_LIO_LIImproves statistical inference accuracy compared with stationary covariance model C_LI