Multi-echo ICA (ME-ICA) Processing of fMRI Data

⚠️ PLEASE NOTE This code base is currently unmaintained. No new feature enhancements or bug fixes will be considered at this time.

We encourage prospective users to instead consider tedana, which maintains and extends many of the multi-echo-specific features of ME-ICA.

For fMRI processing more generally, we refer users to AFNI or fMRIPrep

This repository is a fork of the (also unmaintained) Bitbucket repository.

Dependencies

  1. AFNI
  2. Python 2.7
  3. numpy
  4. scipy

Installation

Install Python and other dependencies. If you have AFNI installed and on your path, you should already have an up-to-date version of ME-ICA on your path. Running meica.py without any options will check for dependencies and let you know if they are met. If you don't have numpy/scipy (or appropriate versions) installed, I would strongly recommend using the Enthought Canopy Python Distribution. Click here for more installation help.

Important Files and Directories

Usage

fMRI data is called: rest_e1.nii.gz, rest_e2.nii.gz, rest_e3.nii.gz, etc. Anatomical is: mprage.nii.gz

meica.py and tedana.py have a number of options which you can view using the -h flag.

Here's an example use:

meica.py -d rest1_e1.nii.gz,rest1_e2.nii.gz,rest1_e3.nii.gz -e 15,30,45 -b 15s -a mprage.nii --MNI --prefix sub1_rest

This means:

-e 15,30,45   are the echo times in milliseconds
-d rest_e1.nii.gz,rest_e2...   are the 4-D time series datasets (comma separated list of dataset of each TE) from a multi-echo fMRI acqusition
-a ...   is a "raw" mprage with a skull
-b   15 means drop first 15 seconds of data for equilibration
--MNI   warp anatomical to MNI space using a built-in high-resolution MNI template. 
--prefix sub1_rest   prefix for final functional output datasets, i.e. sub1_rest_....nii.gz

Again, see meica.py -h for handling other situations such as: anatomical with no skull, no anatomical at all, applying FWHM smoothing, non-linear warp to standard space, etc.

Click here more info on group analysis.

Output

For a step-by-step guide on how to assess ME-ICA results in more detail, click here

Some Notes