<p>Neural activity data can be associated with behavioral and physiological variables by analyzing their changes in the temporal domain. However, such relationships are often difficult to quantify and test, requiring advanced computational modeling approaches. Here, we provide a protocol for the statistical analysis of brain dynamics and for testing their associations with behavioral, physiological and other non-imaging variables. The protocol is based on an open-source Python package built on a generalization of the hidden Markov model (HMM)—the Gaussian-linear HMM—and supports multiple experimental modalities, including task-based and resting-state studies, often used to explore a wide range of questions in neuroscience and mental health. Our toolbox is available as both a Python library and a graphical interface, so it can be used by researchers with or without programming experience. Statistical inference is performed by using permutation-based methods and structured Monte Carlo resampling, and the framework can easily handle confounding variables, multiple testing corrections and hierarchical relationships within the data, among other features. The package includes tools developed to facilitate the intuitive visualization of statistical results, along with comprehensive documentation and step-by-step tutorials for data interpretation. Overall, the protocol covers the full workflow for the statistical analysis of functional neural data and their temporal dynamics.</p>