Improved estimation of dynamic connectivity from resting-state fMRI data

Functional magnetic resonance imaging (fMRI) has been widely used for neuronal connectivity analysis. As a datadriven technique, independent component analysis (ICA) has become a valuable tool for fMRI studies. Recently, due to the dynamic nature of the human brain, time-varying connectivity analysis is regarded as an important measure to reveal essential information within the network. The sliding window approach has been commonly used to extract dynamic information from fMRI time series. However, it has some limitations due to the assumption that connectivity at a given time can be estimated from all the samples of the input time series data spanned by the selected window. To address this issue, we apply a time-varying graphical lasso model (TVGL) proposed by Hallac et al., which can infer the network even when the observation interval is at only one time point. On the other hand, recent results have shown that the individual's connectivity profiles can be used as "fingerprint" to identify subjects from a large group. We hypothesize that the subject-specific FC profiles may have the critical effect on analyzing FC dynamics at a group level. In this work, we apply a group ICA (GICA) based data-driven framework to assess dynamic functional network connectivity (dFNC), based on the combination of GICA and TVGL. Also, we use the regression model to remove the subject-specific individuality in detecting functional dynamics. The results prove our hypothesis and suggest that removing the individual effect may benefit us to assess the connectivity dynamics within the human brain.