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.