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. TE-dependent analysis (tedana) is a Python module for denoising multi-echo functional magnetic resonance imaging (fMRI) data.

About¶

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

tedana originally came about as a part of the ME-ICA pipeline. The ME-ICA pipeline originally performed both pre-processing and TE-dependent analysis of multi-echo fMRI data; however, tedana now assumes that you’re working with data which has been previously preprocessed.

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. 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.

3. 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):