OSL Dynamics#
A Python toolbox for studying dynamic functional brain networks in M/EEG and fMRI data: from raw recordings to parcel time courses, generative models of network dynamics, and statistical significance testing.
Install from conda-forge or PyPI, and check your GPU is working.
A quick introduction: a typical workflow and a short example.
Each stage of an analysis on real data, as scripts and notebooks you can download.
Preprocessing, coregistration, source reconstruction, parcellation and sign flipping.
The HMM, DyNeMo and other generative models for dynamic brain networks.
GLM permutation tests with maximum statistic, cluster-based and TFCE corrections.
The brain parcellations available in osl-dynamics.
Common questions on preparing data, training models and post-hoc analysis.
Every class and function, with its parameters.
If you find this toolbox useful, please cite:
Gohil, C., Huang, R., Roberts, E., van Es, M. W., Quinn, A. J., Vidaurre, D., & Woolrich, M. W. (2024). osl-dynamics, a toolbox for modeling fast dynamic brain activity. eLife, 12, RP91949.