Understanding the temporal dynamics of functional brain connectivity is important for addressing various questions in network neuroscience, such as how connectivity affects cognition and changes with disease. A fundamental challenge is to evaluate whether connectivity truly exhibits dynamics, or simply is static. The most common approach uses sliding-window methods to model functional connectivity over time, and this is often combined with frequentist hypothesis testing frameworks to evaluate dynamics. However, this requires defining appropriate sampling distributions and hyperparameters, such as window length, which imposes specific assumptions on the dynamics. Here, we explore how these assumptions influence the detection of dynamic connectivity, and introduce an alternative approach based on Bayesian hypothesis testing with Wishart processes. This framework estimates uncertainty in the connectivity estimates, and uses this to provide strength of evidence for both dynamic and static connectivity. It encodes assumptions through prior distributions, allowing prior knowledge on the time-dependent structure of connectivity to be incorporated into the model. Using simulations, we compare the two approaches and demonstrate how different assumptions affect the detection of dynamic connectivity. Finally, by applying both approaches to an fMRI working-memory task, we find that conclusions at the group-level increase robustness to modeling choices. Our work highlights the importance of carefully considering modeling assumptions when evaluating dynamic connectivity.

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