comet.connectivity

class comet.connectivity.CoactivationPatterns(time_series: ndarray | list, n_states: int = 5, subject_clusters: int = 5, random_state: int | None = None, n_init: int = 50, progress_bar: bool = True)[source]

Bases: ConnectivityMethod

Co-activation patterns (state-based connectivity).

References

Torabi, M., Mitsis, G. D., & Poline, J. B. (2024). On the variability of dynamic functional connectivity assessment methods. GigaScience, 13, giae009. https://doi.org/10.1093/gigascience/giae009

Parameters:

  • time_series (list or np.ndarray) – The input time series data.

  • n_states (int, optional) – Number of states for the method. Default is 5.

  • subject_clusters (int, optional) – Number of subject clusters. Default is 5.

cluster_ts(act, n_clusters)[source]
estimate()[source]

Estimate state-based connectivity

Returns:

  • np.ndarray – State time course (n_subjects x T)

  • np.ndarray – Connectivity states (P x P x n_states)

name = 'STATE Co-activation Patterns'
class comet.connectivity.ConnectivityMethod(time_series, diagonal=0, fisher_z=False, tril=False)[source]

Bases: object

Base class for all dynamic functional connectivity methods.

time_series

Time series data.

Type:

np.ndarray

T

Number of timepoints.

Type:

int

P

Number of parcels.

Type:

int

diagonal

Value to set on the diagonal of connectivity matrices.

Type:

int or float

fisher_z

Whether to apply Fisher z-transformation.

Type:

bool

tril

Whether to return only the lower triangle of the matrices.

Type:

bool

abstract estimate()[source]

Abstract method to compute the connectivity matrix. This method should be implemented in each child class.

Raises:

NotImplementedError – If the method is not implemented in the child class.

postproc()[source]

Post-process the connectivity matrix with optional Fisher z-transformation, z-standardization, diagonal setting, and lower triangle extraction.

Returns:

The post-processed connectivity matrix.

Return type:

np.ndarray

class comet.connectivity.ContinuousHMM(time_series: ndarray | list, n_states: int = 5, hmm_iter: int = 20, progress_bar: bool = True)[source]

Bases: ConnectivityMethod

Continuous hidden markov model (state-based connectivity).

References

Torabi, M., Mitsis, G. D., & Poline, J. B. (2024). On the variability of dynamic functional connectivity assessment methods. GigaScience, 13, giae009. https://doi.org/10.1093/gigascience/giae009

Parameters:

  • time_series (list or np.ndarray) – The input time series data.

  • n_states (int, optional) – Number of states for the method. Default is 5.

  • hmm_iter (int, optional) – Number of iterations for the HMM. Default is 20.

estimate()[source]

Estimate state-based connectivity

Returns:

  • np.ndarray – State time course (n_subjects x T)

  • np.ndarray – Connectivity states (P x P x n_states)

name = 'STATE Continuous Hidden Markov Model'
class comet.connectivity.DCC(time_series: ndarray, num_cores: int = 16, standardizeData: bool = True, progress_bar: bool = True, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Dynamic Conditional Correlation (DCC) as described by Lindquist et al. (2014).

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • num_cores (int, optional) – Number of CPU cores to use for parallel processing. Default is 16.

  • standardizeData (bool, optional) – Whether to standardize the time series data. Default is True.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

References

Lindquist, M. A., Xu, Y., Nebel, M. B., & Caffo, B. S. (2014). Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. NeuroImage, 101, 531-546. https://doi.org/10.1016/j.neuroimage.2014.06.052

estimate()[source]

DCC algorithm

Parameters:

theta (T-by-N matrix) – fMRI time series data

Returns:

R – estimated dynamic conditional correlation tensor

Return type:

N*N*T np.ndarray

name = 'CONT Dynamic Conditional Correlation'
class comet.connectivity.DiscreteHMM(time_series: ndarray | list, n_states: int = 5, state_ratio: float = 0.6, subject_clusters: int = 5, windowsize: int = 29, shape: Literal['rectangular', 'gaussian', 'hamming'] = 'gaussian', std: float = 10, stepsize: int = 15, hmm_iter: int = 20, progress_bar: bool = True)[source]

Bases: ConnectivityMethod

Discrete hidden markov model (state-based connectivity).

References

Torabi, M., Mitsis, G. D., & Poline, J. B. (2024). On the variability of dynamic functional connectivity assessment methods. GigaScience, 13, giae009. https://doi.org/10.1093/gigascience/giae009

Parameters:

  • time_series (list or np.ndarray) – The input time series data.

  • n_states (int, optional) – Number of states for the method. Default is 5.

  • state_ratio (float, optional) – Ratio of states to use for clustering. Default is 3/5.

  • subject_clusters (int, optional) – Number of subject clusters. Default is 5.

  • windowsize (int, optional) – Size of the sliding window. Default is 29.

  • shape (str, optional) – Shape of the window. Default is “gaussian”.

  • std (float, optional) – Standard deviation for gaussian window. Default is 10.

  • stepsize (float, optional) – Step size for the sliding window. Default is 15.

  • hmm_iter (int, optional) – Number of iterations for the HMM. Default is 20.

estimate()[source]

Estimate state-based connectivity

Returns:

  • np.ndarray – State time course (n_subjects x T)

  • np.ndarray – Connectivity states (P x P x n_states)

name = 'STATE Discrete Hidden Markov Model'
class comet.connectivity.EdgeConnectivity(time_series: ndarray, method: Literal['eTS', 'eFC'] = 'eTS', standardizeData: bool = True, labels: list = None, vlim: float = 3)[source]

Bases: ConnectivityMethod

Edge-centric connectivity method.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • method (string, optional) –

    The specific connectivity to calculate. Default is “eTS”.

    • eTS: returns the edge time series (edges x time)

    • eFC: returns the edge functional connectivity (edges x edges x time).

  • standardizeData (bool, optional) – Whether to standardize the time series data. Default is True.

  • vlim (float, optional) – Limit for plotting in the GUI (not used in the method itself). Default is 3.

References

Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature neuroscience, 23(12), 1644–1654. DOI: https://doi.org/10.1038/s41593-020-00719-y

estimate()[source]

Calculate edge-centric connectivity (eTS or eFC).

Returns:

Dynamic functional connectivity depending on the method.

For eTS: Time x Edge array. For eFC: Edge x Edge x Time array.

Return type:

np.ndarray

name = 'CONT Edge-centric Connectivity'
class comet.connectivity.FlexibleLeastSquares(time_series: ndarray, standardizeData: bool = True, mu: float = 100.0, num_cores: int = 4, progress_bar: bool = True, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Flexible Least Squares connectivity method.

This implementation estimates dynamic functional connectivity using the Flexible Least Squares (FLS) algorithm as described in the DynamicBC toolbox.

For each source region i, the FLS linear system is built once and solved against all target time series simultaneously. Parallel execution across regions is supported via joblib.

Parameters:

  • time_series (np.ndarray) – The input time series data of shape (T, P), where T is the number of time points and P the number of regions.

  • standardizeData (bool, optional) – Whether to standardize the time series column-wise before estimation. Default is True.

  • mu (float, optional) – Regularization parameter controlling the smoothness of betas. Default is 100.0.

  • num_cores (int, optional) – Number of CPU cores to use for parallel processing. Set to 1 for single-core execution. Default is 4.

  • progress_bar (bool, optional) – Whether to display a progress bar during estimation. Default is True.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation in post-processing. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

dfc

The dynamic connectivity estimates of shape (P, P, T).

Type:

np.ndarray

N_estimates

Number of estimates (equals the number of time points T).

Type:

int

References

Liao, W., Wu, G. R., Xu, Q., Ji, G. J., Zhang, Z., Zang, Y. F., & Lu, G. (2014). DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis. Brain Connectivity, 4(10), 780–790. https://doi.org/10.1089/brain.2014.0253

estimate() → ndarray[source]

Estimate dynamic functional connectivity using FLS.

Returns:

Dynamic connectivity as a (P, P, T) array, symmetrized and post-processed according to class settings.

Return type:

np.ndarray

name = 'CONT Flexible Least Squares'
class comet.connectivity.Jackknife(time_series: ndarray, windowsize: int = 1, stepsize: int = 1, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Jackknife correlation method.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • windowsize (int, optional) – Size of the sliding window. Default is 1.

  • stepsize (int, optional) – Step size for sliding the window. Default is 1.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

References

Richter CG, Thompson WH, Bosman CA, Fries P. A jackknife approach to quantifying single-trial correlation between covariance-based metrics undefined on a single-trial basis. https://doi.org/10.1016/j.neuroimage.2015.04.040

centers()[source]

Calculate the central index of each window so dynamic functional connectivity (dFC) estimates can be related to the original time series.

Returns:

Central index of each window.

Return type:

np.ndarray

estimate()[source]

Calculate jackknife correlation.

Returns:

Dynamic functional connectivity as a PxPxN array.

Return type:

np.ndarray

name = 'CONT Jackknife Correlation'
class comet.connectivity.KSVD(time_series: ndarray | list, n_states: int = 5)[source]

Bases: ConnectivityMethod

Windowless state-based connectivity based on K-SVD.

References

Torabi, M., Mitsis, G. D., & Poline, J. B. (2024). On the variability of dynamic functional connectivity assessment methods. GigaScience, 13, giae009. https://doi.org/10.1093/gigascience/giae009

Rubinstein, R., Zibulevsky, M., & Elad, M. (2008). Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. Cs Technion, 40(8), 1-15.

Parameters:

  • time_series (list or np.ndarray) – The input time series data.

  • n_states (int, optional) – Number of states for the method. Default is 5.

estimate()[source]

Estimate state-based connectivity

Returns:

  • np.ndarray – State time course (n_subjects x T)

  • np.ndarray – Connectivity states (P x P x n_states)

name = 'STATE K-SVD'
class comet.connectivity.LeiDA(time_series: ndarray, flip_eigenvectors: bool = True, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Leading Eigenvector Dynamics.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • flip_eigenvectors (bool, optional) – The sign of each leading eigenvector is adjusted so that the majority of its elements are negative. This ensures consistent orientation of eigenvectors across time points and subjects. Default is True.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

References

Cabral, J., Vidaurre, D., Marques, P., Magalhães, R., Silva Moreira, P., Miguel Soares, J., … & Kringelbach, M. L. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Scientific reports, 7(1), 5135. https://doi.org/10.1038/s41598-017-05425-7

Olsen, A. S., Lykkebo-Valløe, A., Ozenne, B., Madsen, M. K., Stenbæk, D. S., Armand, S., … & Fisher, P. M. (2022). Psilocybin modulation of time-varying functional connectivity is associated with plasma psilocin and subjective effects. Neuroimage, 264, 119716. https://doi.org/10.1016/j.neuroimage.2022.119716

Vohryzek, J., Deco, G., Cessac, B., Kringelbach, M. L., & Cabral, J. (2020). Ghost attractors in spontaneous brain activity: Recurrent excursions into functionally-relevant BOLD phase-locking states. Frontiers in systems neuroscience, 14, 20. https://doi.org/10.3389/fnsys.2020.00020

estimate()[source]

Calculate Leading Eigenvector Dynamics Analysis (LeiDA). The leading eigenvectors are saved in as the V1 attribute.

Returns:

Dynamic functional connectivity as a PxPxN array.

Return type:

np.ndarray

name = 'CONT Leading Eigenvector Dynamics'
class comet.connectivity.PhaseSynchronization(time_series: ndarray, method: Literal['crp', 'phcoh', 'teneto'] = 'crp', diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Instantaneous Phase Synchronization methods.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • method ({'crp', 'phcoh', 'teneto'}, optional) – The phase synchrony method to use. Default is ‘crp’.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

References

Honari, H., Choe, A. S., & Lindquist, M. A. (2021). Evaluating phase synchronization methods in fMRI: A comparison study and new approaches. NeuroImage, 228, 117704. https://doi.org/10.1016/j.neuroimage.2020.117704

estimate()[source]

Abstract method to compute the connectivity matrix. This method should be implemented in each child class.

Raises:

NotImplementedError – If the method is not implemented in the child class.

name = 'CONT Phase Synchronization'
class comet.connectivity.SlidingWindow(time_series: ndarray, windowsize: int = 29, stepsize: int = 1, shape: Literal['rectangular', 'gaussian', 'hamming'] = 'rectangular', std: float = 10, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Sliding Window connectivity method.

This is the most widely used dynamic functional connectivity method. It involves sliding a window over the data. Covariance is estimated for each windowed section.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • windowsize (int, optional) – Size of the sliding window. Default is 29.

  • stepsize (int, optional) – Step size for sliding the window. Default is 1.

  • shape ({'rectangular', 'gaussian', 'hamming'}, optional) – Shape of the window. Default is ‘rectangular’.

  • std (float, optional) – Standard deviation for the Gaussian window. Default is 10.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

centers()[source]

Calculate the central index of each window so dynamic functional connectivity (dFC) estimates can be related to the original time series.

Returns:

Central index of each window.

Return type:

np.ndarray

estimate()[source]

Calculate sliding window correlation.

Returns:

Dynamic functional connectivity as a PxPxN array.

Return type:

np.ndarray

name = 'CONT Sliding Window'
class comet.connectivity.SlidingWindowClustering(time_series: ndarray | list, n_states: int = 5, subject_clusters: int = 5, windowsize: int = 29, shape: Literal['rectangular', 'gaussian', 'hamming'] = 'gaussian', std: float = 10, stepsize: int = 15, random_state: int | None = None, n_init: int = 50, progress_bar: bool = True)[source]

Bases: ConnectivityMethod

Siding window clustering (SWC) state-based connectivity (2-level clustering).

References

Torabi, M., Mitsis, G. D., & Poline, J. B. (2024). On the variability of dynamic functional connectivity assessment methods. GigaScience, 13, giae009. https://doi.org/10.1093/gigascience/giae009

Parameters:

  • time_series (list or np.ndarray) – The input time series data.

  • n_states (int, optional) – Number of states for the method. Default is 5.

  • subject_clusters (int, optional) – Number of clusters for the first level clustering. Default is 5.

  • windowsize (int, optional) – Size of the sliding window. Default is 29.

  • shape (str, optional) – Shape of the window. Default is “gaussian”.

  • std (float, optional) – Standard deviation for gaussian window. Default is 10.

  • stepsize (int, optional) – Step size for the sliding window. Default is 15.

estimate()[source]

Estimate state-based connectivity

Returns:

  • np.ndarray – State time course (n_subjects x T)

  • np.ndarray – Connectivity states (P x P x n_states)

mat2vec(C_t)[source]
name = 'STATE Sliding Window Clustering'
vec2mat(F, N)[source]
class comet.connectivity.SpatialDistance(time_series: ndarray, dist: Literal['euclidean', 'cosine', 'cityblock'] = 'euclidean', diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Spatial Distance connectivity method.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • dist ({'euclidean', 'cosine', 'cityblock'}, optional) – Type of distance metric to use. Default is ‘euclidean’.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

References

William Hedley Thompson, Per Brantefors, Peter Fransson. From static to temporal network theory: Applications to functional brain connectivity. https://doi.org/10.1162/NETN_a_00011

estimate()[source]

Calculate spatial distance correlation.

Returns:

Dynamic functional connectivity as a PxPxN array.

Return type:

np.ndarray

name = 'CONT Spatial Distance'
class comet.connectivity.Static_Covariance(time_series: ndarray, cov_estimator: Literal['LedoitWolf', None] = None, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Static functional connectivity method using covariance.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • cov_estimator (str, optional) – Shrinkage for covariance estimation. Can be None or Ledoit-Wolf. Default is None.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

estimate()[source]

Estimate the functional connectivity.

Returns:

Static functional connectivity matrix.

Return type:

np.ndarray

name = 'STATIC Covariance'
class comet.connectivity.Static_Mutual_Info(time_series: ndarray, num_bins: int = 10, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Static functional connectivity method using mutual information.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • num_bins (int, optional) – Number of bins to use for the mutual information calculation. Default is 10.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

estimate()[source]

Estimate the functional connectivity.

Returns:

Static functional connectivity matrix.

Return type:

np.ndarray

name = 'STATIC Mutual Information'
class comet.connectivity.Static_Partial(time_series: ndarray, cov_estimator: Literal['LedoitWolf', None] = None, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Static functional connectivity method using partial correlation.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • cov_estimator (str, optional) – Shrinkage for covariance estimation. Can be None or Ledoit-Wolf. Default is None.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

estimate()[source]

Estimate the functional connectivity.

Returns:

Static functional connectivity matrix.

Return type:

np.ndarray

name = 'STATIC Partial Correlation'
class comet.connectivity.Static_Pearson(time_series: ndarray, cov_estimator: Literal[None, 'LedoitWolf'] = None, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Static functional connectivity method using Pearson correlation.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • shrinkage (str, optional) – Shrinkage for covariance estimation. Can be None or Ledoit-Wolf. Default is None.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

estimate()[source]

Estimate the functional connectivity.

Returns:

Static functional connectivity matrix.

Return type:

np.ndarray

name = 'STATIC Pearson Correlation'
class comet.connectivity.TemporalDerivatives(time_series: ndarray, windowsize: int = 7, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Multiplication of Temporal Derivatives connectivity method.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • windowsize (int, optional) – Size of the sliding window. Default is 7.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

References

Shine JM, Koyejo O, Bell PT, Gorgolewski KJ, Gilat M, Poldrack RA. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. https://doi.org/10.1016/j.neuroimage.2015.07.064.

centers()[source]

Calculate the central index of each window so dynamic functional connectivity (dFC) estimates can be related to the original time series.

Returns:

Central index of each window.

Return type:

np.ndarray

estimate()[source]

Calculate multiplication of temporal derivatives.

Returns:

Dynamic functional connectivity as a PxPxN array.

Return type:

np.ndarray

name = 'CONT Multiplication of Temporal Derivatives'
class comet.connectivity.WaveletCoherence(time_series: ndarray, method: Literal['weighted'] = 'weighted', TR: float = 0.72, fmin: float = 0.007, fmax: float = 0.15, n_scales: int = 15, drop_scales: int = 2, drop_timepoints: int = 50, progress_bar: bool = True, diagonal: int = 0, fisher_z: bool = False, tril: bool = False)[source]

Bases: ConnectivityMethod

Instantaneous Wavelet Coherence.

Parameters:

  • time_series (np.ndarray) – The input time series data.

  • method ({'weighted'}, optional) – The method to use for calculating wavelet coherence. Default is ‘weighted’.

  • TR (float, optional) – Repetition time of the data. Default is 0.72.

  • fmin (float, optional) – Minimum frequency for wavelet transform. Default is 0.007.

  • fmax (float, optional) – Maximum frequency for wavelet transform. Default is 0.15.

  • n_scales (int, optional) – Number of scales for wavelet transform. Default is 15.

  • drop_scales (int, optional) – Number of scales to drop from the edges. Default is 2.

  • drop_timepoints (int, optional) – Number of time points to drop from the edges. Default is 50.

  • diagonal (int, optional) – Value to set on the diagonal of connectivity matrices. Default is 0.

  • fisher_z (bool, optional) – Whether to apply Fisher z-transformation. Default is False.

  • tril (bool, optional) – Whether to return only the lower triangle of the matrices. Default is False.

References

Jacob Billings, Manish Saggar, Jaroslav Hlinka, Shella Keilholz, Giovanni Petri; Simplicial and topological descriptions of human brain dynamics. Network Neuroscience 2021; 5 (2): 549–568. https://doi.org/10.1162/netn_a_00190

estimate()[source]

Calculate instantaneous wavelet coherence.

Returns:

Dynamic functional connectivity as a PxPxN array.

Return type:

np.ndarray

name = 'CONT Wavelet Coherence'