Tedana: expanding flexibility for multi-echo fMRI noise removal methods
Poster No:
1527
Submission Type:
Abstract Submission
Authors:
Daniel Handwerker1, Eneko Uruñuela2, David Abbott3, Peter Bandettini1, Logan Dowdle4, Marta Gómez5, Javier Gonzalez-Castillo1, Sarah Goodale6, Neha Reddy7, Robert Smith3, Bahman Tahayori3, Taylor Salo8
Institutions:
1National Institute of Mental Health, Bethesda, MD, 2University of Calgary, Calgary, Canada, 3The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, 4Maastricht University, Maastricht, Netherlands, 5University of the Basque Country, Donostia, Spain, 6Vanderbilt University Medical Center, Nashville, TN, 7Northwestern University, Chicago, IL, 8University of Pennsylvania, Philadelphia, PA
First Author:
Co-Author(s):
Introduction:
Multi-echo fMRI acquisitions are gaining popularity with many shared datasets (me-ica.github.io/open-multi-echo-data/) and with ongoing usage in several large-scale studies. Tedana is a multi-echo-based denoising software [DuPre, Salo 2021, Kundu 2012, Posse 1999] that can be integrated into AFNI [Cox 1996] or fMRIPrep [Esteban et al 2018] preprocessing pipelines, or run independently. The contributors to tedana openly share documentation, educational materials and other resources to support usage and development of multi-echo fMRI methods. Tedana contributors also respond to questions on neurostars.org using the "multi-echo" and "tedana" tags. We here describe several features recently added to increase the utility and flexibility of the tedana software package.
Methods:
The past year of tedana development (github.com/ME-ICA/tedana) has included several key improvements of practical benefit while adding options for exploratory development of multi-echo fMRI: (1) In addition to multi-echo metrics, external time series-such as head motion or respiration-can be fit to ICA components, making it possible to compare the tedana approach to other ICA denoising methods and merge complementary methods. (2) It is easier to add and calculate new component metrics to use in the denoising process. (3) An iterative Robust ICA [Anglada-Girotto 2022] method was added to increase reproducibility. (4) Quality control reporting now includes additional measures that help identify variations in data quality.
Results:
To demonstrate the additional regressor fitting options, we included in a tedana analysis of an existing hand-grasp task dataset [Reddy 2023 & 2024] 6 motion parameters and an average CSF time series. Several components otherwise accepted by the "minimal decision tree" in tedana were rejected by the revised decision tree based on their correlation with these additional nuisance regressors. An alternative tree accepts components that would be rejected based on this criterion but exhibit adequate correlation with a task design. (Fig1). These novel acceptance vs rejection criteria are subjective, and optimal choice may depend on one's tolerance for false positives vs false negatives with respect to the hypothesis being tested. The added regressors nevertheless provide more information for making such decisions and can be used to further improve these methods.
·Figure 1
Conclusions:
Early demonstration of these added functionalities in tedana show how tedana is evolving in stability and flexibility, catalyzing novel method development for multi-echo fMRI.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Exploratory Modeling and Artifact Removal 2
Methods Development 1
Neuroinformatics and Data Sharing:
Workflows
Keywords:
Data analysis
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
MRI
MRI PHYSICS
Open-Source Code
Open-Source Software
Workflows
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cox R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and biomedical research, an international journal, 29(3), 162–173. https://doi.org/10.1006/cbmr.1996.0014
DuPre, Salo et al., (2021). “TE-dependent analysis of multi-echo fMRI with tedana.” Journal of Open Source Software, 6(66), 3669 https://doi.org/10.21105/joss.03669
Esteban, O., Markiewicz, C.J., Blair, R.W. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16, 111–116 (2019). https://doi.org/10.1038/s41592-018-0235-4
Kundu, Inati, Evans, Luh, Bandettini (2012). Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. NeuroImage, 60(3), 1759-1770. https://doi.org/10.1016/j.neuroimage.2011.12.028.
Posse, S., Wiese, S., Gembris, D., Mathiak, K., Kessler, C., Grosse-Ruyken, M.-L., Elghahwagi, B., Richards, T., Dager, S.R. and Kiselev, V.G. (1999), Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging. Magn. Reson. Med., 42: 87-97. https://doi.org/10.1002/(sici)1522-2594(199907)42:1%3C87::aid-mrm13%3E3.0.co;2-o
Reddy NA, Zvolanek KM, Bright MG. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA [Dataset]. OpenNeuro 2023. https://doi.org/10.18112/openneuro.ds004662.v1.1.0.
Reddy NA, Zvolanek KM, Moia S, Caballero-Gaudes C, Bright MG. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA. Imaging Neuroscience 2024;2:1–30. https://doi.org/10.1162/imag_a_00057.