pactools / Pactools
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============================= Getting Started with pactools
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This package provides tools to estimate phase-amplitude coupling (PAC) in neural time series.
In particular, it implements the driven auto-regressive (DAR)
models presented in the reference below [Dupre la Tour et al. 2017
_].
Read more in the documentation <https://pactools.github.io>
_.
Installation
To install pactools
, use one of the following two commands:
-
Latest stable version::
pip install pactools
-
Development version::
pip install git+https://github.com/pactools/pactools.git#egg=pactools
To upgrade, use the --upgrade
flag provided by pip
.
To check if everything worked fine, you can do::
python -c 'import pactools'
and it should not give any error messages.
Phase-amplitude coupling (PAC)
Among the different classes of cross-frequency couplings,
phase-amplitude coupling (PAC) - i.e. high frequency activity time-locked
to a specific phase of slow frequency oscillations - is by far the most
acknowledged.
PAC is typically represented with a comodulogram, which shows the strenght of
the coupling over a grid of frequencies.
Comodulograms can be computed in pactools
with more
than 10 different methods.
.. include:: generated/backreferences/pactools.Comodulogram.examples .. raw:: html
<div style='clear:both'></div>
Driven auto-regressive (DAR) models
One of the method is based on driven auto-regressive (DAR) models. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model goodness of fit via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to such model-based approach.
We recommend using DAR models to estimate PAC in neural time-series.
More detail in [Dupre la Tour et al. 2017
_].
.. include:: generated/backreferences/pactools.dar_model.DAR.examples .. raw:: html
<div style='clear:both'></div>
Acknowledgment
This work was supported by the ERC Starting Grant SLAB ERC-YStG-676943 to Alexandre Gramfort, the ERC Starting Grant MindTime ERC-YStG-263584 to Virginie van Wassenhove, the ANR-16-CE37-0004-04 AutoTime to Valerie Doyere and Virginie van Wassenhove, and the Paris-Saclay IDEX NoTime to Valerie Doyere, Alexandre Gramfort and Virginie van Wassenhove,
Cite this work
If you use this code in your project, please cite
[Dupre la Tour et al. 2017
_]:
.. code-block::
@article{duprelatour2017nonlinear,
author = {Dupr{\'e} la Tour, Tom and Tallot, Lucille and Grabot, Laetitia and Doy{\`e}re, Val{\'e}rie and van Wassenhove, Virginie and Grenier, Yves and Gramfort, Alexandre},
journal = {PLOS Computational Biology},
publisher = {Public Library of Science},
title = {Non-linear auto-regressive models for cross-frequency coupling in neural time series},
year = {2017},
month = {12},
volume = {13},
url = {https://doi.org/10.1371/journal.pcbi.1005893},
pages = {1-32},
number = {12},
doi = {10.1371/journal.pcbi.1005893}
}
.. _Dupre la Tour et al. 2017: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005893