Models for infering dynamics in neuroimaging data
Project description
Models for analysing neuroimaging data developed by the Oxford Centre for Human Brain Activity (OHBA) group at the University of Oxford.
- Models included:
Hidden Markov Model (HMM).
Dynamic Network Modes (DyNeMo).
Multi-Dynamic Network Modes (M-DyNeMo).
Dynamic Network States (DyNeSt).
Single-dynamic Adversarial Generator Encoder (SAGE).
Multi-dynamic Adversarial Generator Encoder (MAGE).
Installation
git clone git@github.com:OHBA-analysis/osl-dynamics.git
cd osl-dynamics
pip install -e .To use the HMM you also need to install armadillo:
conda install -c conda-forge armadilloSee CONTRIBUTION.md for further details.
Documentation
The documentation is hosted on Read the Docs: https://osl-dynamics.readthedocs.io. To compile locally use:
cd osl-dynamics
python setup.py build_sphinxSee CONTRIBUTION.md for further details.
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