Multi-echo fMRI course at OHBM 2026 and tedana v26.0.3
tedana-newsletter
Multi-echo fMRI course at OHBM 2026
There will be a full-day educational course on “Practical Multi-echo fMRI: Underlying principles, challenges, potential, and applications” at OHBM 2026 in Bordeaux, France on Sunday, June 14, 2026!
Please consider attending and share with students and other researchers.
The target audience will be researchers and clinicians who are using or thinking about acquiring or using multi-echo fMRI data and people who want to better understand and evaluate multi-echo fMRI studies.
The learning objectives for the course are:
To understand why multi-echo fMRI can be used to identify non-neural noise based on MRI physic principals and how to think about the cost and benefits of multi-echo fMRI data acquisition.
To understand the current state of multi-echo fMRI noise removal and analysis methods with a focus on both practical guidance and open areas for future research.
To learn about active basic science and clinical studies that are using multi-echo fMRI and the benefits and lessons learned from those studies.
The scheduled speakers are: Mélanie Garcia, Javier Gonzalez-Castillo, Eric Pierre, Eui-Jin Jung, Julia Moser, Kathryn Lamar-Bruno, Nathan Spreng, Logan Dowdle, and Eneko Uruñuela.
If you have questions, you can reach out to the organizers: Daniel Handwerker, Sarah Goodale, and Eneko Uruñuela.
tedana v26.0.3 released!
Enhancements since our last newsletter (we didn’t send a message for v25.1.0 through v26.0.2) include new and improved visualizations, features, and bug fixes. Notable changes include:
Enhancements:
For the results report, you can now use left & right arrow keys to cycle through the components in the pie chart. The contour maps for the different masks also has improved visualization (#1333)
Tedana now passes just the voxels in the mask between functions, which can substantially reduce memory requirements. (#1305)
Better integration between RICA and tedana. RICA is an interactive visualization of components that allows users to view the full component table and manually change component classifications. When you run tedana, you can run “python open_rica_report.py” in the results output directory to launch RICA.
Nonlinear fitting of T2*/S0 estimation and RobustICA are better parallelized, which may reduce processing time. (#1266)
We added an option to exclude noisy volumes when running t2smap (#1248)
We switched to using uv to manage dependencies and to support our internal testing, but this may also make it easier to deploy consistent versions of tedana in pipelines (#1229 & #1316)
Breaking changes, bugs, and future deprecations:
Breaking change: Each component has a metric for “variance explained” (0-100%) and “normalized variance explained” (0-1). These are now both 0-100% and the relationship between the two is explained better (#1310)
Breaking change: z-maps for the ICA weight maps were calculated using a non-standard metric that we replaced with a standard definition of a z statistic. More details about what was previously done and what is now done are in #1332
Bug fix: User-inputted dummy scans were not properly processed in v26.0.0 to 26.0.2. (#1402)
Bug fix: We had some “fun” with various versions of tedana and nilearn (hence the rapid release of the 26.0.0, 0.1 and 0.2 versions). Please use v26.0.3 and not the earlier v26 versions.
Echo times are expected to be provided in seconds. Echo times that are in milliseconds are automatically detected and changed, but this may be deprecated in the future. #1287
Since Python ended support for python 3.9 and it was causing some issues within tedana, the minimum python version for tedana is now 3.10.
If you are making your own decision tree, a few things that used to raise warnings will now stop the program with an error (#1370)
Full release notes are here: https://github.com/ME-ICA/tedana/releases
Getting help with tedana or multi-echo fMRI
Questions about multi-echo fMRI and tedana usage or development can be posted at https://neurostars.org with tedana or multi-echo tags or as an issue or discussion at https://github.com/ME-ICA/tedana or https://mattermost.brainhack.org/brainhack/channels/tedana. We actively monitor all three message boards and try to efficiently respond.
Contributors
We are excited to welcome new contributors: @miltoncmacho, @mauspad, and @cmahlen. We continue to thank all of our contributors for their continued input and help with tedana. We always look forward to seeing more new faces! If you’re not sure where to start, please feel free to open an issue on github, ask a question on neurostars with the ‘tedana’ or ‘multi-echo’ tag, join our next monthly developer’s call, or join the tedana mattermost channel.