Poster No:
1336
Submission Type:
Abstract Submission
Authors:
Daniel Handwerker1, Peter Bandettini1, Logan Dowdle2, Elizabeth DuPre3, Javier Gonzalez-Castillo1, Christopher Markiewicz3, Stefano Moia4, Peter Molfese1, Neha Reddy5, Taylor Salo6, Joshua Teves1, Eneko Uruñuela7
Institutions:
1National Institute of Mental Health, Bethesda, MD, 2Center for Magnetic Resonance Research, Minneapolis, MN, 3Stanford University, Stanford, CA, 4Maastricht University, Maastricht, Netherlands, 5Northwestern University, Chicago, IL, 6University of Pennsylvania, Philadelphia, PA, 7Basque Center on Cognition, Brain and Language, Gipuzkoa, Spain
First Author:
Co-Author(s):
Taylor Salo
University of Pennsylvania
Philadelphia, PA
Joshua Teves
National Institute of Mental Health
Bethesda, MD
Eneko Uruñuela
Basque Center on Cognition, Brain and Language
Gipuzkoa, Spain
Introduction:
Multi-echo fMRI involves the collection of data at multiple echo times for each excitation pulse. Three or more volumes of fMRI data can be collected at every time point with minimal acquisition cost. These additional data can be used to reduce acquisition noise and better estimate and remove signals that are not blood oxygen level dependent [Kundu 2012, Posse 1999]. Tedana [DuPre, Salo 2021] is an open source software that provides ways to apply multi-echo noise removal methods. Tedana also includes educational resources to help researchers better understand and use multi-echo fMRI data, whether or not they use tedana's software.
Methods:
Tedana uses modern software techniques combined with an accessible community of developers and users to continue to improve the software (github.com/ME-ICA/tedana) and resources (tedana.readthedocs.io). Tedana is integrated into the AFNI [Cox 1996] and fMRIPrep [Esteban et al 2018] preprocessing pipelines. We also monitor and respond to questions at neurostars.org using the "multi-echo" and "tedana" tags to better support our software and have built a welcoming community of multi-echo fMRI users.
Results:
A major software update over the past year was modularization of the ICA classification "decision tree." The central multi-echo fMRI denoising method in tedana applies ICA to the data and then selects which components should be classified as noise and removed [Kundu 2012]. In the modularized code, each step in the decision tree can be defined in a text file, and all classifications that change in each step are fully tracked. With this new system, it is now possible to alter the decision process to address study-specific needs. The new process and how to design a new decision tree are fully documented to make new innovations within tedana accessible to more researchers.
We have additionally improved our methods for tracking and automatically enforcing code style consistency and removed unnecessary dependencies that limited the integration of tedana with other software packages. In responding to user questions, we have also identified and fixed several issues with the code or documentation.
Conclusions:
The changes during the past year make it easier for new contributors to join and will support planned improvements to denoising methods. A key planned improvement is to allow external information, such as motion regressors, into the decision process so that multi-echo methods can be combined with other ICA-based denoising methods, like AROMA [Pruim et al 2015].
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Methods Development 2
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
Provide references using author date format
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
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage, 112, 267–277. https://doi.org/10.1016/j.neuroimage.2015.02.064