tedana.gscontrol.minimum_image_regression

minimum_image_regression(optcom_ts, mmix, mask, comptable, io_generator)[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:

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

  • mmix ((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 optcom_ts

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

  • comptable ((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.

  • 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 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 denoising (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:

Filename

Content

sphis_hik.nii

T1-like effect

hik_ts_OC_MIR.nii

T1-corrected BOLD (high-Kappa) time series

dn_ts_OC_MIR.nii

Denoised version of T1-corrected time series

betas_hik_OC_MIR.nii

T1 global signal-corrected components

meica_mix_MIR.1D

T1 global signal-corrected mixing matrix

References