FCEst is a package for estimating static and time-varying functional connectivity (TVFC) in Python.
It includes a range of methods for this task, including Wishart processes, DCC and GO MGARCH models, and sliding windows.
A comparison and benchmarking of these estimation methods can be found here.
FCEst can easily be installed by cloning this repository and using pip install:
$ git clone https://github.com/OnnoKampman/FCEst.git
$ cd FCEst
$ pip install -e .$ pip install git+https://github.com/OnnoKampman/[email protected]Make sure you have R installed and that R_HOME is set, for example by running brew install r on MacOS.
At some point this package will be made directly available from PyPi.
Below is a short example demonstrating how to use the high-level API for TVFC estimation.
Additional model demonstrations can be found in Jupyter Notebooks under .notebooks/Model demos/.
>>> import numpy as np
>>> N = 200 # number of time steps (scanning volumes)
>>> D = 3 # number of time series (ROIs)
>>> x = np.linspace(0, 1, N).reshape(-1, 1)
>>> y = np.random.random(size=(N, D))>>> from fcest.models.wishart_process import VariationalWishartProcess
>>> m = VariationalWishartProcess(
x_observed=x,
y_observed=y,
)
>>> tvfc_estimates, tvfc_estimates_stddev = m.predict_corr(x)>>> from fcest.models.wishart_process import SparseVariationalWishartProcess
>>> m = SparseVariationalWishartProcess(
D=D,
Z=x,
)
>>> tvfc_estimates, tvfc_estimates_stddev = m.predict_corr(x)>>> from fcest.models.mgarch import MGARCH
>>> m = MGARCH(
mgarch_type="DCC",
)
>>> m.fit_model(y)
>>> tvfc_estimates = m.predict_corr()>>> from fcest.models.sliding_windows import SlidingWindows
>>> sw = SlidingWindows(
x_train_locations=x,
y_train_locations=y,
)
>>> tvfc_estimates = sw.overlapping_windowed_cov_estimation(
window_length=30,
)>>> from fcest.models.sliding_windows import SlidingWindows
>>> sw = SlidingWindows(
x_train_locations=x,
y_train_locations=y,
)
>>> cv_window_length = sw.compute_cross_validated_optimal_window_length()
>>> tvfc_estimates = sw.overlapping_windowed_cov_estimation(
window_length=cv_window_length,
)>>> from fcest.models.sliding_windows import SlidingWindows
>>> sw = SlidingWindows(
x_train_locations=x,
y_train_locations=y,
)
>>> sfc_estimates = sw.estimate_static_functional_connectivity()FCEst is an open-source project and contributions from the community are more than welcome. Please raise an issue on Github or send me a message.
A curated list of publications related to functional connectivity estimation can be found on Semantic Scholar.