ArticleImaging neuroscience (Cambridge, Mass.)2025
The Gaussian-linear hidden Markov model: A Python package.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A comprehensive framework for statistical testing of brain dynamics.Nature protocols · 2026Review
- Brain states recur across diverse narrative contexts during longitudinal viewing.bioRxiv : the preprint server for biology · 2026Article
- Dissociable, species-specific impact of Aβ on static and dynamic functional connectomes.bioRxiv : the preprint server for biology · 2026Article
- Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences.bioRxiv : the preprint server for biology · 2026Article
- Targeted Time-Varying Functional Connectivity.Human brain mapping · 2025Article
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Authors and funding
7 authors.
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Abstract
We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses-including unsupervised, encoding, and decoding models. GLHMM is available as a Python toolbox with an emphasis on statistical testing and out-of-sample prediction-that is, aimed at finding and characterising brain-behaviour associations. The toolbox uses a stochastic variational inference approach, enabling it to handle large data sets at reasonable computational time. The GLHMM can work with various types of data, including animal recordings or non-brain data, and is suitable for a broad range of experimental paradigms. For demonstration, we show examples with fMRI, local field potential, electrocorticography, magnetoencephalography, and pupillometry.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.