tedana: TE Dependent ANAlysis

The tedana package is part of the ME-ICA pipeline, performing TE-dependent analysis of multi-echo functional magnetic resonance imaging (fMRI) data.

https://circleci.com/gh/ME-ICA/tedana.svg?style=svg http://img.shields.io/badge/License-LGPL%202.0-blue.svg


When using tedana, please include the following citations:

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.

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(40), 16187-16192.

DuPre, E. M., Salo, T., Markello, R. D., Kundu, P., Whitaker K. J. (2018). ME-ICA/tedana: Initial tedana release (Version 0.0.1). Zenodo. doi:10.5281/zenodo.1250562.

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

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