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.

Citations

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

License Information

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

Contents:

• Introduction
  • Derivatives
• Usage
  • Command line options
• Contributing to tedana
  • Style Guide
• API
  • tedana.model: Modeling TE-dependence
  • tedana.decomposition: Data decomposition
  • tedana.selection: Component selection
  • tedana.workflows: Common workflows
  • tedana.utils: Utility functions

Indices and tables

• Index
• Module Index
• Search Page