Introduction¶
tedana
works by decomposing multi-echo BOLD data via PCA and ICA.
These components are then analyzed to determine whether they are TE-dependent
or -independent. TE-dependent components are classified as BOLD, while
TE-independent components are classified as non-BOLD, and are discarded as part
of data cleaning.
Derivatives¶
medn
- ‘Denoised’ BOLD time series after: basic preprocessing, T2* weighted averaging of echoes (i.e. ‘optimal combination’), ICA denoising. Use this dataset for task analysis and resting state time series correlation analysis.
tsoc
- ‘Raw’ BOLD time series dataset after: basic preprocessing and T2* weighted averaging of echoes (i.e. ‘optimal combination’). ‘Standard’ denoising or task analyses can be assessed on this dataset (e.g. motion regression, physio correction, scrubbing, etc.) for comparison to ME-ICA denoising.
*mefc
- Component maps (in units of delta S) of accepted BOLD ICA components. Use this dataset for ME-ICR seed-based connectivity analysis.
mefl
- Component maps (in units of delta S) of ALL ICA components.
ctab
- Table of component Kappa, Rho, and variance explained values, plus listing of component classifications.