Functional magnetic resonance imaging (fMRI) has been widely used for neuronal connectivity analysis. As a datadriventechnique, independent component analysis (ICA) has become a valuable tool for fMRI studies. Recently, due tothe dynamic nature of the human brain, time-varying connectivity analysis is regarded as an important measure to revealessential information within the network. The sliding window approach has been commonly used to extract dynamicinformation from fMRI time series. However, it has some limitations due to the assumption that connectivity at a giventime can be estimated from all the samples of the input time series data spanned by the selected window. To address thisissue, we apply a time-varying graphical lasso model (TVGL) proposed by Hallac et al., which can infer the networkeven when the observation interval is at only one time point. On the other hand, recent results have shown that theindividual’s connectivity profiles can be used as “fingerprint” to identify subjects from a large group. We hypothesizethat 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-specificindividuality in detecting functional dynamics. The results prove our hypothesis and suggest that removing theindividual effect may benefit us to assess the connectivity dynamics within the human brain.
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