tedana: TE Dependent ANAlysis

Latest Version PyPI - Python Version JOSS DOI Zenodo DOI CircleCI License Documentation Status Codecov Average time to resolve an issue Percentage of issues still open Join the chat Join our tinyletter mailing list Code style: black

About

TE-de``pendent ``ana``lysis (``tedana) is a Python library for denoising multi-echo functional magnetic resonance imaging (fMRI) data. tedana originally came about as a part of the [ME-ICA](https://github.com/me-ica/me-ica) pipeline, although it has since diverged. An important distinction is that while the ME-ICA pipeline originally performed both pre-processing and TE-dependent analysis of multi-echo fMRI data, tedana now assumes that you’re working with data which has been previously preprocessed.

https://user-images.githubusercontent.com/7406227/40031156-57b7cbb8-57bc-11e8-8c51-5b29f2e86a48.png

For a summary of multi-echo fMRI, which is the imaging technique tedana builds on, visit Multi-echo fMRI.

For a detailed procedure of how tedana analyzes the data from multi-echo fMRI, visit Processing pipeline details.

Citations

When using tedana, please include the following citations:

tedana Available from: https://doi.org/10.5281/zenodo.1250561

2. DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S., Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S., Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M., Whitaker, K., & Handwerker, D. A. (2021). TE-dependent analysis of multi-echo fMRI with tedana. Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669.

3. Kundu, P., Inati, S. J., Evans, J. W., Luh, W. M., & Bandettini, P. A. (2011). Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. NeuroImage, 60, 1759-1770.

4. Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J., Saad, Z. S., Bandettini, P. A., & Bullmore, E. T. (2013). Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proceedings of the National Academy of Sciences, 110, 16187-16192.

Alternatively, you can automatically compile relevant citations by running your tedana code with duecredit. For example, if you plan to run a script using tedana (in this case, tedana_script.py):

python -m duecredit tedana_script.py

You can also learn more about why citing software is important.

Posters

_images/tedana-ohbm2019-poster.png _images/tedana-ohbm2018-poster.png

License Information

tedana is licensed under GNU Lesser General Public License version 2.1.

Contents:

Indices and tables