tedana.decomposition.tedica

tedica(data, n_components, fixed_seed, ica_method='fastica', n_robust_runs=30, maxit=500, maxrestart=10, n_threads=1)[source]

Perform ICA on data with the user selected ica method and returns mixing matrix.

Parameters:

  • data ((Mc x T) numpy.ndarray) – Dimensionally reduced optimally combined functional data, where Mc is samples in classification mask, T is time

  • n_components (int) – Number of components retained from PCA decomposition.

  • fixed_seed (int) – Seed for ensuring reproducibility of ICA results.

  • ica_method (:obj: str) – selected ICA method, can be fastica or robustica.

  • n_robust_runs (:obj: int) – selected number of robust runs when robustica is used. Default is 30.

  • maxit (int, optional) – Maximum number of iterations for ICA. Default is 500.

  • maxrestart (int, optional) – Maximum number of restarted decompositions to perform with different random seeds if ICA fails to converge. ICA will stop running if there is convergence prior to reaching this limit. Default is 10.

  • n_threads (int, optional) – Number of threads to use for parallel computation. Default is 1.

Returns:

  • mixing ((T x C) numpy.ndarray) – Z-scored mixing matrix for converting input data to component space, where C is components and T is the same as in data

  • fixed_seed (int) – Random seed from final decomposition.