A comprehensive framework for statistical testing of brain dynamics
Journal:
arXiv
Published Date:
May 5, 2025
Abstract
We introduce a comprehensive statistical framework for analysing brain
dynamics and testing their associations with behavioural, physiological and
other non-imaging variables. Based on a generalisation of the Hidden Markov
Model (HMM) - the Gaussian-Linear HMM - our open-source Python package supports
multiple experimental paradigms, including task-based and resting-state
studies, and addresses a wide range of questions in neuroscience and related
scientific fields. Inference is carried out 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. The package includes tools for intuitive
visualisation of statistical results, along with comprehensive documentation
and step-by-step tutorials to make it accessible for users of varying
expertise. Altogether, it provides a broadly applicable, end-to-end pipeline
for analysis and statistical testing of functional neural data and its
dynamics.
