TY - JOUR
T1 - The Gaussian-linear hidden Markov model
T2 - A Python package
AU - Vidaurre, Diego
AU - Masaracchia, Laura
AU - Larsen, Nick Y.
AU - Ruijters, Lenno R.P.T.
AU - Alonso, Sonsoles
AU - Ahrends, Christine
AU - Woolrich, Mark W.
N1 - Publisher Copyright:
© 2025 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
PY - 2025/2/3
Y1 - 2025/2/3
N2 - 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.
AB - 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.
KW - brain dynamics
KW - hidden Markov model
KW - multimodal analysis
KW - neuroinformatics software
KW - out-of-sample predictions
KW - statistical testing
UR - http://www.scopus.com/inward/record.url?scp=105000171334&partnerID=8YFLogxK
U2 - 10.1162/imag_a_00460
DO - 10.1162/imag_a_00460
M3 - Journal article
AN - SCOPUS:105000171334
SN - 2837-6056
VL - 3
JO - Imaging Neuroscience
JF - Imaging Neuroscience
M1 - imag_a_00460
ER -