spectral_connectivity

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spectral_connectivity is a python software package that computes frequency-domain brain connectivity measures such as coherence, spectral granger causality, and the phase lag index using the multitaper Fourier transform. Although there are other python packages that do this (see nitime and MNE-Python), spectral has several differences:

it is designed to handle multiple time series at once
it caches frequently computed quantities such as the cross-spectral matrix and minimum-phase-decomposition, so that connectivity measures that use the same processing steps can be more quickly computed.
it decouples the time-frequency transform and the connectivity measures so that if you already have a preferred way of computing Fourier coefficients (i.e. from a wavelet transform), you can use that instead.
it implements the non-parametric version of the spectral granger causality in Python.
it implements the canonical coherence, which can efficiently summarize brain-area level coherences from multielectrode recordings.
easier user interface for the multitaper fourier transform

See the notebooks (#1, #2) for more information on how to use the package.

Documentation

See the documentation here.

Implemented Measures

Functional

coherency
canonical_coherence
imaginary_coherence
phase_locking_value
phase_lag_index
weighted_phase_lag_index
debiased_squared_phase_lag_index
debiased_squared_weighted_phase_lag_index
pairwise_phase_consistency

Directed

directed_transfer_function
directed_coherence
partial_directed_coherence
generalized_partial_directed_coherence
direct_directed_transfer_function
group_delay
phase_lag_index
pairwise_spectral_granger_prediction

Package Dependencies

spectral_connectivity requires:

python=3.5
numpy
matplotlib
scipy
xarray

See environment.yml for the most current list of dependencies.

Installation

pip install spectral_connectivity

conda install -c edeno spectral_connectivity

Developer Installation

If you want to make contributions to this library, please use this installation.

Install miniconda (or anaconda) if it isn't already installed. Type into bash (or install from the anaconda website):

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
hash -r

Clone the repository to your local machine (.../spectral_connectivity) and install the anaconda environment for the repository. Type into bash:
```
conda update -q conda
conda info -a
conda env create -f environment.yml
source activate spectral_connectivity
python setup.py develop
```

Usage Example

from spectral_connectivity import Multitaper, Connectivity

m = Multitaper(time_series=signals,
               sampling_frequency=sampling_frequency,
               time_halfbandwidth_product=time_halfbandwidth_product,
               time_window_duration=0.060,
               time_window_step=0.060,
               start_time=time[0])
c = Connectivity.from_multitaper(m)

coherence = c.coherence_magnitude()
weighted_phase_lag_index = c.weighted_phase_lag_index()
canonical_coherence = c.canonical_coherence(brain_area_labels)

Future Directions

We hope to take advantage of the labeled data of the xarray package.