Resources

Journal articles describing multi-echo methods

• Publications using multi-echo fMRI catalogues papers using multi-echo fMRI,

  with information about acquisition parameters.
• Multi-echo acquisition

  Posse, NeuroImage 2012

  Includes an historical overview of multi-echo acquisition and research
• Multi-Echo fMRI A Review of Applications in fMRI Denoising and Analysis of BOLD Signals

  Kundu et al, NeuroImage 2017

  A review of multi-echo denoising with a focus on the MEICA algorithm
• Enhanced identification of BOLD-like components with MESMS and MEICA

  Olafsson et al, NeuroImage 2015

  The appendix includes a good explanation of the math underlying MEICA denoising
• Comparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions

  Dipasquale et al, PLoS One 2017

  The appendix includes some recommendations for multi-echo acquisition

Videos

• An educational session from OHBM 2017 by Dr. Prantik Kundu about multi-echo denoising

• A series of lectures from the OHBM 2017 multi-echo session on multiple facets of multi-echo data analysis

• Multi-echo fMRI lecture from the 2018 NIH FMRI Summer Course by Javier Gonzalez-Castillo

  Slides from 2018 NIH FMRI Summer Course

Multi-echo preprocessing software

tedana requires data that has already been preprocessed for head motion, alignment, etc.

AFNI can process multi-echo data natively as well as apply tedana denoising through the use of afni_proc.py. To see various implementations, start with Example 12 in the afni_proc.py help

fmriprep can also process multi-echo data, but is currently limited to using the optimally combined timeseries. For more details, see the fmriprep workflows page.

Currently SPM and FSL do not natively support multi-echo fmri data processing.

Other software that uses multi-echo fMRI

tedana represents only one approach to processing multi-echo data. Currently there are a number of methods that can take advantage of or use the information contained in multi-echo data. These include:

• 3dMEPFM: A multi-echo implementation of ‘paradigm free mapping’, that is

  detection of neural events in the absence of a prespecified model. By

  leveraging the information present in multi-echo data, changes in relaxation

  time can be directly estimated and more events can be detected.

  For more information, see the following paper.
• Bayesian approach to denoising: An alternative approach to separating out

  BOLD and non-BOLD signals within a Bayesian framework is currently under

  development.
• Multi-echo Group ICA: Current approaches to ICA just use a single run of

  data in order to perform denoising. An alternative approach is to use

  information from multiple subjects or multiple runs from a single subject

  in order to improve the classification of BOLD and non-BOLD components.
• Dual Echo Denoising: If the first echo can be collected early enough,

  there are currently methods that take advantage of the very limited BOLD

  weighting at these early echo times.
• qMRLab: This is a MATLAB software package for quantitative magnetic

  resonance imaging. While it does not support ME-fMRI, it does include methods

  for estimating T2*/S0 from high-resolution, complex-valued multi-echo GRE

  data with correction for background field gradients.

Datasets

A number of multi-echo datasets have been made public so far. This list is not necessarily up to date, so please check out OpenNeuro to potentially find more.

• Multi-echo fMRI replication sample of autobiographical memory, prospection and theory of mind reasoning tasks

• Multi-echo Cambridge

• Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity

• Valence processing differs across stimulus modalities

• Cambridge Centre for Ageing Neuroscience (Cam-CAN)

Publications using multi-echo fMRI

You can view and suggest additions to this spreadsheet here This is a volunteer-led effort so, if you know of a excluded publication, whether or not it is yours, please add it.