The tedana roadmap
Project vision
tedana was originally developed as a place for the multi-echo fMRI
denoising method that was originally defined in ME-ICA
(ME-ICA source).
tedana was designed to be more understandable, modular, and adaptable so
that it can serve as a testing ground for novel multi-echo fMRI denoising
methods.
We have expanded to welcome additional multi-echo fMRI processing
approaches, and to support communal resources for multi-echo fMRI, whether
or not they directly involve the tedana software.
Scope of tedana
tedana is a collection of tools, software and a community related to echo time (TE) dependent analyses. The umbrella of tedana covers a number of overlapping, but somewhat distinct, ideas related to multi-echo analysis. This scope includes collecting multi-echo data (Acquisition), combining those echoes together (Combination), with optional noise removal (Denoising), inspecting the outputs (Visualization) and answering multi-echo related questions (Community). In general, tedana accepts previously preprocessed data to produce outputs that are ready for further analyses.
Acquisition
While the development of multi-echo sequences is beyond the current scope of tedana, the tedana community is committed to providing guidelines on current multi-echo implementations. This will include both specific instructions for how to collect multi-echo data for multiple vendors as well as details about what types of data have been collected thus far. These details are subject to change, and are intended to provide users with an idea of what is possible, rather than definitive recommendations.
Our focus is on functional MRI, including both magnitude and phase data, however we understand that quantitative mapping has the potential to aid in data processing. Thus, we believe that some details on non-functional MRI acquisitions, such as detailed T2* mapping, may fall within the scope of tedana. Acquisition related details can be found in the tedana Documentation.
Combining echoes
An early step in processing data collected with multiple echoes is the combination of the data into a single time series. We currently implement multiple options to combine multi-echo data and will add more as they continue to be developed. This is an area of active development and interest.
Denoising
tedana was developed out of a package known as multi-echo ICA, ME-ICA, or MEICA developed by Dr. Prantik Kundu. Though the usage of ICA for classification of signal vs noise components has continued in tedana, this is not a rule. The tedana community is open and encouraging of new denoising methods, whether or not they have a basis in ICA.
Specifically, we are interested in any method that seeks to use the information from multiple echoes to identify signal (defined here as BOLD signals arising from neural processing) and noise (defined here as changes unrelated to neural processing, such as motion, cardiac, respiration).
tedana is primarily intended to work on volume data, that is, data that is still in structured voxel space. This is because several of the currently used denoising metrics rely on spatial continuity, and they have not yet been updated to consider continuity over cortical vertices. Therefore, surface-based denoising is not currently within the scope of tedana, but code could be written so that it is a possible option in the future.
Currently tedana works on a single subject, run by run basis; however, methods that use information across multiple runs are welcome.
Visualization
As part of the processing stream, tedana provides figures and an HTML-based report for inspecting results. These are intended to help users understand the outputs from tedana and diagnose problems. Though a comprehensive viewer (such as fsleyes) is outside of the scope of tedana, we will continue to improve the reports and add new information as needed.
Community
tedana is intended to be a community of multi-echo users. The primary resource is the github repository and related documentation. In addition, the tedana group will attempt to answer multi-echo related questions on NeuroStars (multi-echo tag or tedana tag).
What tedana isn’t
While the list of things that do not fall under the scope of tedana are infinite, it is worth mentioning a few points:
tedana will not offer a GUI for usage
it is intended to be either a stand alone processing package or serve as a processing step as part of a larger package (i.e. fmriprep or afni_proc.py).
tedana will not provide basic preprocessing steps, such as motion correction or slice timing correction. While these were previously part of the ME-ICA pipeline, the sheer variety of possible choices, guidelines and data types precludes including it within the tedana package.
tedana will not provide statistical analyses in the form of general linear models, connectivity or decoding. Though multi-echo data is amenable to all methods of analysis, these methods will not be included in the tedana package.
Metrics of success and corresponding milestones
We will know that we have been successful in creating tedana when we have succeeded in providing
several concrete deliverables, which can be broadly categorized into:
Each deliverable has been synthesized into a milestone that gives the tedana community a link
between the issues and the high level vision for the project.
Documentation
Summary: One long-standing concern with ME-EPI denoising has been the availability of documentation for the method outside of published scientific papers. To address this, we have created a ReadTheDocs site; however, there are still several sections either explicitly marked as “#TODO” or otherwise missing crucial information.
We are committed to providing helpful documentation for all users of tedana.
One metric of success, then, is to develop documentation that includes:
Motivations for conducting echo time dependent analysis,
A collection of key ME-EPI references and acqusition sequences from the published literature,
Tutorials on how to use
tedana,The different processing steps that are conducted in each workflow,
An up-to-date description of the API,
A transparent explanation of the different decisions that are made through the
tedanapipeline, andWhere to seek support
This milestone will close when the online documentation contains the minimum necessary information to orient a complete newcomer to ME-EPI, both on the theoretical basis of the method as well as the practical steps used in ME-EPI denoising.
Transparent and reproducible processing
Summary: Alongside the lack of existing documentation, there is a general unfamiliarity with how selection criteria are applied to individual data sets. This lack of transparency, combined with the non-deterministic nature of the decomposition, has generated significant uncertainty when interpreting results.
In order to build and maintain confidence in ME-EPI processing,
any analysis software—including tedana—must provide enough information such that
the user is empowered to conduct transparent and reproducible analyses.
This will permit clear reporting of the ME-EPI results in published studies
and facilitate a broader conversation in the scientific community on the nature of ME-EPI processing.
We are therefore committed to making tedana analysis transparent and reproducible
such that we report back all processing steps applied to any individual data set,
including the specific selection criteria used in making denoising decisions.
This, combined with the reproducibility afforded by seeding all non-deterministic steps,
will enable both increased confidence and better reporting of ME-EPI results.
A metric of success for tedana then, should be enhancements to the code such that:
Non-deterministic steps are made reproducible by enabling access to a “seed value”, and
The decision process for individual component data is made accessible to the end user.
This milestone will close when when the internal decision making process for component selection is made accessible to the end user, and an analysis can be reproduced by an independent researcher who has access to the same data.
Testing
Summary:
Historically, the lack of testing for ME-EPI analysis pipelines has prevented new
developers from engaging with the code for fear of silently breaking or otherwise degrading
the existing implementation.
Moving forward, we want to grow an active development community,
where developers feel empowered to explore new enhancements to the tedana code base.
One means to ensure that new code does not introduce bugs is through extensive testing. We are therefore committed to implementing high test coverage at both the unit test and integration test levels; that is, both in testing individual functions and broader workflows, respectively.
A metric of success should thus be:
Achieving 90% test coverage for unit tests, as well as
Three distinguishable integration tests over a range of possible acquisition conditions.
This milestone will close when we have 90% test coverage for unit tests and three distinguishable integration tests, varying number of echos and acquisition type (i.e., task vs. rest).
Workflow integration: AFNI
Summary:
Currently, afni_proc.py distributes an older version of tedana,
around which they have built a wrapper script, tedana_wrapper.py, to ensure compatibility.
AFNI users at this point are therefore not accessing the latest version of tedana.
We will grow our user base if tedana can be accessed through AFNI,
and we are therefore committed to supporting native integration of tedana in AFNI.
One metric of success, therefore, will be if we can demonstrate sufficient stability and support
such that the afni_proc.py maintainers are willing to switch to tedana as the recommended
method of accessing ME-EPI denoising in AFNI.
We will aim to aid in this process by increasing compatibility between tedana
and the afni_proc.py workflow, eliminating the need for an additional wrapper script.
This milestone will close when tedana is stable enough such that the recommended default in
afni_proc.py is to access ME-EPI denoising via pip install tedana,
rather than maintaining the alternative version that is currently used.
Workflow integration: BIDS
Summary:
Currently, the BIDS ecosystem has limited support for ME-EPI processing.
We will grow our user base if tedana is integrated into existing BIDS Apps and
therefore accessible to members of the BIDS community.
One promising opportunity is if tedana can be used natively in FMRIPrep.
Some of the work is not required at this repository, but other changes will need to happen here;
for example, making sure the outputs are BIDS compliant.
A metric of success, then, will be:
Fully integrating
tedanaintoFMRIPrep, andMaking
tedanaoutputs compliant with the BIDS derivatives specification.
This milestone will close when the denoising steps of tedana are stable enough
to integrate into FMRIPrep and the FMRIPrep project is updated to process ME-EPI scans.
Method extensions & improvements
Summary: Overall, each of the listed deliverables will support a broader goal: to improve on ME-EPI processing itself. This is an important research question and will advance the state-of-the-art in ME-EPI processing.
A metric of success here would be * EITHER integrating a new decomposition method, beyond ICA * OR validating new selection criteria.
To achieve either of these metrics, it is likely that we will need to incoporate a
quality-assurance module into tedana, possibly as visual reports.
This milestone will close when the codebase is stable enough to integrate novel methods
into tedana, and that happens!
Developing a healthy community
Summary:
In developing tedana, we are committed to fostering a healthy community.
A healthy community is one in which the maintainers are happy and not overworked,
and which empowers users to contribute back to the project.
By making tedana stable and well-documented, with enough modularity to integrate improvements,
we will enable new contributors to feel that their work is welcomed.
We therefore have one additional metric of success:
An outside contributor integrates an improvement to ME-EPI denoising.
This milestone will probably never close,
but will serve to track issues related to building and supporting the tedana community.