spectral_connectivity

What is spectral_connectivity?

spectral_connectivity is a Python software package that computes multitaper spectral estimates and 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_connectivity 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
all function are GPU-enabled if cupy is installed and the environmental variable SPECTRAL_CONNECTIVITY_ENABLE_GPU is set to 'true'.

Tutorials

See the following notebooks for more information on how to use the package:

Usage Example

from spectral_connectivity import Multitaper, Connectivity

# Compute multitaper spectral estimate
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])

# Sets up computing connectivity measures/power from multitaper spectral estimate
c = Connectivity.from_multitaper(m)

# Here are a couple of examples
power = c.power() # spectral power
coherence = c.coherence_magnitude()
weighted_phase_lag_index = c.weighted_phase_lag_index()
canonical_coherence = c.canonical_coherence(brain_area_labels)

Documentation

See the documentation here.

Citation

For citation, please use the following:

Denovellis, E.L., Myroshnychenko, M., Sarmashghi, M., and Stephen, E.P. (2022). Spectral Connectivity: a python package for computing multitaper spectral estimates and frequency-domain brain connectivity measures on the CPU and GPU. JOSS 7, 4840. 10.21105/joss.04840.

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
global coherence

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
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 env create -f environment.yml
conda activate spectral_connectivity
pip install -e .

Recent publications and pre-prints that used this software

Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality https://doi.org/10.1007/978-3-662-58485-9_11
Network Path Convergence Shapes Low-Level Processing in the Visual Cortex https://doi.org/10.3389/fnsys.2021.645709
Subthalamic–Cortical Network Reorganization during Parkinson's Tremor https://doi.org/10.1523/JNEUROSCI.0854-21.2021
Unifying Pairwise Interactions in Complex Dynamics https://doi.org/10.48550/arXiv.2201.11941
Phencyclidine-induced psychosis causes hypersynchronization and disruption of connectivity within prefrontal-hippocampal circuits that is rescued by antipsychotic drugs https://doi.org/10.1101/2021.02.03.429582
The cerebellum regulates fear extinction through thalamo-prefrontal cortex interactions in male mice https://doi.org/10.1038/s41467-023-36943-w

Eden-Kramer-Lab/spectral_connectivity