Journal articles describing multi-echo methods¶
- Publications using multi-echo fMRI catalogues papers using multi-echo fMRI,with information about acquisition parameters.
- Posse, NeuroImage 2012Includes an historical overview of multi-echo acquisition and research
- Kundu et al, NeuroImage 2017A review of multi-echo denoising with a focus on the MEICA algorithm
- Olafsson et al, NeuroImage 2015The appendix includes a good explanation of the math underlying MEICA denoising
- Dipasquale et al, PLoS One 2017The appendix includes some recommendations for multi-echo acquisition
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 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.
- 3dMEPFM: A multi-echo implementation of ‘paradigm free mapping’, that isdetection of neural events in the absence of a prespecified model. Byleveraging the information present in multi-echo data, changes in relaxationtime 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 outBOLD and non-BOLD signals within a Bayesian framework is currently underdevelopment.
- Multi-echo Group ICA: Current approaches to ICA just use a single run ofdata in order to perform denoising. An alternative approach is to useinformation from multiple subjects or multiple runs from a single subjectin 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 BOLDweighting at these early echo times.
- qMRLab: This is a MATLAB software package for quantitative magneticresonance imaging. While it does not support ME-fMRI, it does include methodsfor estimating T2*/S0 from high-resolution, complex-valued multi-echo GREdata with correction for background field gradients.
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)