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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
cupyis installed and the environmental variableSPECTRAL_CONNECTIVITY_ENABLE_GPUis set to 'true'.
See the following notebooks for more information on how to use the package:
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)See the documentation here.
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.
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
spectral_connectivity requires:
- python
- numpy
- matplotlib
- scipy
- xarray
See environment.yml for the most current list of dependencies.
pip install spectral_connectivityor
conda install -c edeno spectral_connectivityIf 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 .- 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
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