spectral_connectivity#

Test, Build, and Publish DOI Binder status PyPI version Anaconda-Server Badge Documentation Status codecov

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

  • core transforms and connectivity calculations support GPU acceleration when cupy is installed and SPECTRAL_CONNECTIVITY_ENABLE_GPU=true is set before importing the package. Public results are returned as NumPy arrays.

Tutorials#

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

Usage Example#

The high-level multitaper_connectivity function runs the multitaper transform and returns a labeled xarray object:

from spectral_connectivity import multitaper_connectivity

coherence = multitaper_connectivity(
    time_series,
    sampling_frequency=sampling_frequency,
    method="coherence_magnitude",
    time_halfbandwidth_product=3,
)

time_series may also be an xarray.DataArray. For DataArray inputs, dimension names define axis roles; positions do not. Common dimension names are inferred and transposed automatically; for domain-specific names, pass time_dim, trial_dim, and signal_dim explicitly. Ambiguous dimensions raise instead of falling back to axis position; when a single unrecognized dimension is left for the one remaining role, it is assigned by elimination and a warning names the assumed mapping. Numeric time coordinates are interpreted as elapsed seconds and numeric sample coordinates as sample numbers, and are used to label output window centers. When sampling_frequency is given it is checked against the time index; when it is omitted, a numeric elapsed-seconds time coordinate infers it (a sample index cannot, having no time scale). Inference also requires enough coordinate precision to resolve the rate reliably; pass sampling_frequency explicitly for low-precision or large-offset time coordinates. A 1-D index on the signal dimension is preserved—including its label type—as the result’s source and target coordinates unless signal_names is supplied. Signal labels must be unique, non-missing, NetCDF-compatible scalar strings, real numbers, datetimes, or timedeltas; integer labels must fit the signed 32-bit range for portable NetCDF3 serialization.

Datetime, timedelta, and object-valued time coordinates are not yet supported. Convert them to numeric elapsed seconds before calling multitaper_connectivity, for example:

da = da.assign_coords(time=(da.time - da.time[0]) / np.timedelta64(1, "s"))

datetime and timedelta signal labels remain valid.

A dask-backed DataArray is rejected; materialize it first with DataArray.compute() (or .load()) and pass the result.

For directed measures, result.sel(source="a", target="b") means influence from a to b. The directed-transfer-function family is available by name as an opt-in method. The lower-level Connectivity methods retain their historical array convention: result[..., i, j] represents j -> i.

For finer control, use the Multitaper and Connectivity classes directly:

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)

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

  1. coherency

  2. canonical_coherence

  3. imaginary_coherence

  4. phase_locking_value

  5. phase_lag_index

  6. weighted_phase_lag_index

  7. debiased_squared_phase_lag_index

  8. debiased_squared_weighted_phase_lag_index

  9. pairwise_phase_consistency

  10. global coherence

Directed

  1. directed_transfer_function

  2. directed_coherence

  3. partial_directed_coherence

  4. generalized_partial_directed_coherence

  5. direct_directed_transfer_function

  6. group_delay

  7. phase_lag_index

  8. pairwise_spectral_granger_prediction

Package Dependencies#

spectral_connectivity requires:

  • python

  • numpy

  • matplotlib

  • scipy

  • xarray

See the repository’s pyproject.toml for the authoritative dependency list.

Installation#

pip install spectral_connectivity

or

conda install -c edeno spectral_connectivity

Developer Installation#

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

  1. 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

  1. 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#