Fig. 1.

An illustration of the GICA-TVGL framework. (A) Group ICA (GICA) decomposes resting-state fMRI data into $C=100$ components. 50 of them are identified as intrinsic connectivity networks (ICNs). GICA1 back reconstruction is used to estimate the spatial maps (SMs, ${\text{S}}_{\text{i}}$) and time courses (TCs, ${\text{R}}_{\text{i}}$) for each subject. (B) Following the GICA analysis, time courses (TCs) are transformed by the Gaussian copula model into ${\text{R}}_{\text{i}}^{\prime }$ for each item. Then, dynamic functional connectivity $\left({\text{Θ}}_i\right)$ is assessed through the TVGL model.