tedana.metrics.dependence.compute_signal_minus_noise_t

compute_signal_minus_noise_t(*, z_maps: ndarray, z_clmaps: ndarray, f_t2_maps: ndarray, value_threshold: float | None = None, proportion_threshold: float | None = None) → Tuple[ndarray, ndarray][source]

Compare signal and noise t-statistic distributions with a two-sample t-test.

Divide voxel-level thresholded F-statistic maps into distributions of signal (voxels in significant clusters) and noise (voxels from non-significant clusters) statistics, then compare these distributions with a two-sample t-test.

Parameters:

  • z_maps ((S x C) array_like) – Z-statistic maps for components, reflecting voxel-wise component loadings.

  • z_clmaps ((S x C) array_like) – Cluster-extent thresholded Z-statistic maps for components.

  • f_t2_maps ((S x C) array_like) – Pseudo-F-statistic maps for components from TE-dependence models. Each voxel reflects the model fit for the component weights to the TE-dependence model across echoes.

  • value_threshold (float, optional) – Threshold for voxel-wise significance in input z_maps. Default is None.

  • proportion_threshold (float, optional) – Proportion threshold for voxel-wise significance in input z_maps. Values between 0 and 100. Default is None.

Returns:

  • signal_minus_noise_t ((C) array_like) – T-statistics from component-wise signal > noise paired t-tests.

  • signal_minus_noise_p ((C) array_like) – P-values from component-wise signal > noise paired t-tests.