In most echo-planar image (EPI) fMRI sequences, only one brain image is acquired at each repetition time, at the rate of radio frequency (RF). In contrast, in multi-echo (ME) fMRI, data are acquired for multiple echo times, resulting in multiple volumes with varying levels of contrast acquired per RF.
The physics of multi-echo fMRI¶
Multi-echo fMRI data is obtained by acquiring multiple TEs (commonly called echo times) for each MRI volume during data collection. While fMRI signal contains important neural information (termed the blood oxygen-level dependent, or BOLD signal, it also contains “noise” (termed non-BOLD signal) caused by things like participant motion and changes in breathing. Because the BOLD signal is known to decay at a set rate, collecting multiple echos allows us to assess whether components of the fMRI signal are BOLD- or non-BOLD. For a comprehensive review, see Kundu et al. (2017).
Why use multi-echo?¶
There are many potential reasons an investigator would be interested in using multi-echo EPI (ME-EPI). Among these are the different levels of analysis ME-EPI enables. Specifically, by collecting multi-echo data, researchers are able to compare results for (1) single-echo, (2) optimally combined, and (3) denoised data. Each of these levels of analysis have their own advantages.
For single-echo: currently, field standards are largely set using single-echo EPI. Because multi-echo is composed of multiple single-echo time series, each of these can be analyzed separately. This allows researchers to benchmark their results.
For optimally combined: Rather than analyzing single-echo time series separately, we can combine them into a “optimally combined time series”. For more information on this combination, see processing pipeline details. Optimally combined data exhibits higher SNR and improves statistical power of analyses in regions traditionally affected by drop-out.
For denoised: Collecting multi-echo data allows access to unique denoising metrics.
tedana is one ICA-based denoising pipeline built on this information.
Other ICA-based denoising methods like ICA-AROMA (Pruim et al. (2015))
have been shown to significantly improve the quality of cleaned signal.
These methods, however, have comparably limited information, as they are designed to work with single-echo EPI. Collecting multi-echo EPI allows us to leverage all of the information available for single-echo datasets, as well as additional information only available when looking at signal decay across multiple TEs. We can use this information to denoise the optimally combined time series.
Recommendations on multi-echo use for someone planning a new study¶
Multi-echo fMRI acquisition sequences and analysis methods are rapidly maturing. Someone who has access to a multi-echo fMRI sequence should seriously consider using it. Multiple studies have shown that a weighted average of the echoes to optimize T2* weighting, sometimes called “optimally combined,” gives a reliable, modest boost in data quality. The optimal combination of echoes can currently be calculated in several software packages including AFNI, fMRIPrep, and tedana. In tedana, the weighted average can be calculated with t2smap If no other acquisition compromises are necessary to acquire multi-echo data, this boost is worthwhile. If other compromises are necessary, consider the life of the data set. If data is being acquired for a discrete study that will be acquired, analyzed, and published in a year or two, it might not be worth making compromises to acquire multi-echo data. If a data set is expected to be used for future analyses in later years, it is likely that more powerful approaches to multi-echo denoising will sufficiently mature and add even more value to a data set.
Other multi-echo denoising methods, such as MEICA, the predecessor to tedana, have shown the potential for much greater data quality improvements, as well as the ability to more accurately separate visually similar signal vs noise, such as scanner based drifts vs slow changes in BOLD signal. These more powerful methods are still being improved, and the algorithms are still changing. Users need to have the time and knowledge to look at the denoising output from every run to make sure denoising worked as intended. If someone wants a push-button way to use multi-echo data to improve data quality, that doesn’t require as deep an inspection of every output, stick with using the weighted average. The developers of tedana look forward to when tedana and other methods have sufficiently stable algorithms, which have been validated on a wide range of data sets, so that we can recommend the wide use of tedana.
Acquisition Parameter Recommendations¶
There is no empirically tested best parameter set for multi-echo acquisition. The guidelines for optimizing parameters are similar to single-echo fMRI. For multi-echo fMRI, the same factors that may guide priorities for single echo fMRI sequences are also relevant. Choose sequence parameters that meet the priorities of a study with regards to spatial resolution, spatial coverage, sample rate, signal-to-noise ratio, signal drop-out, distortion, and artifacts.
The one difference with multi-echo is a slight time cost. For multi-echo fMRI, the shortest echo time (TE) is essentially free since it is collected in the gap between the RF pulse and the single-echo acquisition. The second echo tends to roughly match the single-echo TE. Additional echoes require more time. For example, on a 3T MRI, if the T2* weighted TE is 30ms for single echo fMRI, a multi-echo sequence may have TEs of 15.4, 29.7, and 44.0ms. In this example, the extra 14ms of acquisition time per RF pulse is the cost of multi-echo fMRI.
One way to think about this cost is in comparison to single-echo fMRI. If a multi-echo sequence has identical spatial resolution and acceleration as a single-echo sequence, then a rough rule of thumb is that the multi-echo sequence will have 10% fewer slices or 10% longer TR. Instead of compromising on slice coverage or TR, one can increase acceleration. If one increases acceleration, it is worth doing an empirical comparison to make sure there isn’t a non-trivial loss in SNR or an increase of artifacts.
A minimum of 3 echoes is recommended for running TE-dependent denoising. While there are successful studies that don’t follow this rule, it may be useful to have at least one echo that is earlier and one echo that is later than the TE one would use for single-echo T2* weighted fMRI.
More than 3 echoes may be useful, because that would allow for more accurate estimates of BOLD and non-BOLD weighted fluctuations, but more echoes have an additional time cost, which would result in either less spatiotemporal coverage or more acceleration. Where the benefits of more echoes balance out the additional costs is an open research question.
We are not recommending specific parameter options at this time. There are multiple ways to balance the slight time cost from the added echoes that have resulted in research publications. We suggest new multi-echo fMRI users examine the Multi-echo fMRI Publications that use multi-echo fMRI to identify studies with similar acquisition priorities, and use the parameters from those studies as a starting point.
- Multi-echo fMRI Publications 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 acqusition
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
Available multi-echo fMRI sequences for multiple vendors¶
Information on multi-echo sequences from Siemens, GE, and Phillips will be added here.
Multi-echo preprocessing software¶
tedana requires data that has already been preprocessed for head motion, alignment, etc. More details on software packages that include preprocessing options specifically for multi-echo fMRI data, such as AFNI and fMRIPrep will be added here.
Other software that uses multi-echo fMRI¶
Information and links to other approaches for denoising multi-echo fMRI data will be added here.
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)