A Method for Estimating Dynamic Functional Network Connectivity Gradients ( dFNGs ) From ICA Captures Smooth Inter‐Network Modulation

作者：Najme Soleimani, Armin Iraji, Theo G.M. van Erp, Ayşenil Belger, Vince D. Calhoun · 发表于：Human Brain Mapping · 年份：2025 · DOI：10.1002/hbm.70262 · 被引用次数：4 · 研究领域：Functional Brain Connectivity Studies、Neural dynamics and brain function、EEG and Brain-Computer Interfaces

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilize fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each time point to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HCs). Functional dysconnectivity between different brain regions has been reported in SZ, yet the neural mechanisms behind it remain elusive. Using resting-state fMRI and ICA on a dataset consisting of 151 SZ patients and 160 age and gender-matched HCs, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, co...