Introduction

meica preprocesses multi-echo datasets and applies multi-echo ICA based on spatially concatenated echoes. It does so in the following steps:

1. Calculates motion parameters based on images with highest contrast (usually the first echo)
2. Applies motion correction and T2*-weighted co-registration parameters
3. Applies standard EPI preprocessing (slice-time correction, etc.)
4. Computes PCA and ICA in conjunction with TE-dependence analysis

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.