tedana.gscontrol.minimum_image_regression

minimum_image_regression(*, data_optcom: ndarray, mixing: ndarray, mask: ndarray, component_table: DataFrame, classification_tags: list, io_generator: OutputGenerator)[source]

Perform minimum image regression (MIR) to remove T1-like effects from BOLD-like components.

While this method has not yet been described in detail in any publications, we recommend that users cite Kundu et al.[1].

Parameters:

  • data_optcom ((S x T) array_like) – Optimally combined time series data

  • mixing ((T x C) array_like) – Mixing matrix for converting input data to component space, where C is components and T is the same as in data_optcom

  • mask ((S,) array_like) – Boolean mask array

  • component_table ((C x X) pandas.DataFrame) – Component metric table. One row for each component, with a column for each metric. The index should be the component number.

  • classification_tags (list of str) – List of classification tags used in the decision tree. This is used to separate “accepted” and “ignored” components.

  • io_generator (tedana.io.OutputGenerator) – The output generating object for this workflow

Notes

Minimum image regression operates by constructing a amplitude-normalized form of the multi-echo high Kappa (MEHK) time series from BOLD-like ICA components, and then taking the voxel-wise minimum over time. This “minimum map” serves as a voxel-wise estimate of the T1-like effect in the time series. From this minimum map, a T1-like global signal (i.e., a 1D time series) is estimated. The component time series in the mixing matrix are then corrected for the T1-like effect by regressing out the global signal time series from each. Finally, the multi-echo denoised (MEDN) and MEHK time series are reconstructed from the corrected mixing matrix and are written out to new files.

This function writes out several files:

IOGenerator Label

Content

“t1 like img”

T1-like effect

“confounds tsv”

A “mir_global_signal” column with the time course of the T1-like effect is added to this TSV.

“mir denoised img”

Denoised version of T1-corrected time series

“ICA MIR mixing tsv”

T1 global signal-corrected mixing matrix

if io_generator.verbose==True

“ICA accepted mir denoised img”

T1-corrected BOLD (high-Kappa) time series

“ICA accepted mir component weights img”

T1 global signal-corrected components

References