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Functional MRI data can inform us about dynamically changing associations between distinct brain regions i.e., dynamic functional connectivity; dFC). A novel combination of the semiparametric models and network measures allowed us to quantify dFC changes during fMRI task.
Specifically, we utilized subject-specific nonparametric estimates of dFC (Kudela et al. 2017) in the additive mixed model framework to obtain the group level dFC estimates. Subsequently, we incorporated dFC estimates in the extension of traditional modularity analysis (Mucha et al. 2010) to get the dynamic division of brain regions into communities with intra-connectivity greater than expected by chance. This dynamic modularity was summarized on the population and subject level at a brain network and region basis by using entropy (a measure of uncertainty in module assignment during fMRI scan).
The above-described approach offers a low-dimensional representation that supports the existence of common functionally-based organization of the brain and provides biologically meaningful results. This novel methodology can be applied to both task and resting state fMRI data.
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