A Method for Estimating Dynamic Functional Network Connectivity Gradients (dFNGs) From ICA Captures Smooth Inter‐Network Modulation - Soleimani, Najme; Iraji, Armin; Erp, Theo G. M.; Belger, Aysenil; Calhoun, Vince D. (Human brain mapping, 2025)
ABSTRACT 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, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD), and visual (VIS) networks in patients, as well as hypoconnectivity in the sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/default mode network (DMN), as well as SC/AUD/SM/cerebellar (CB) and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/SC networks and transmodal CC/DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM, and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in SZ patients. By employing dFNG, we highlight a new perspective to capture large‐scale fluctuations across the brain while maintaining the convenience of brain networks and low‐dimensional summary measures. The resting‐state functional magnetic resonance imaging (fMRI) data were preprocessed using standard procedures, and then spatially constrained independent component analysis (ICA) was run on the data using the Neuromark fMRI 1.0 template, resulting in 53 intrinsic connectivity networks (ICNs). Next, functional network connectivity (FNC) was calculated both in static and dynamic sense. A diffusion map (gradients) was computed for each static and windowed‐FNC, and each matrix was reordered based on its gradient, followed by k‐means clustering of the reordered dynamic functional network connectivity (dFNCs). This resulted in five dynamic FNC gradients (dFNGs). By employing dFNG, we highlight a new perspective to capture large‐scale fluctuations across the brain while maintaining the convenience of brain networks and low‐dimensional summary measures.