TE-dependence analysis was performed on input data using the tedana workflow \citep{dupre2021te}. An initial mask was generated from the first echo using nilearn's compute_epi_mask function. An adaptive mask was then generated, in which each voxel's value reflects the number of echoes with 'good' data. A two-stage masking procedure was applied, in which a liberal mask (including voxels with good data in at least the first echo) was used for optimal combination, T2*/S0 estimation, and denoising, while a more conservative mask (restricted to voxels with good data in at least the first three echoes) was used for the component classification procedure. A monoexponential model was fit to the data at each voxel using log-linear regression in order to estimate T2* and S0 maps. For each voxel, the value from the adaptive mask was used to determine which echoes would be used to estimate T2* and S0. Multi-echo data were then optimally combined using the T2* combination method \citep{posse1999enhancement}. The following metrics were calculated: kappa, rho, countnoise, countsigFT2, countsigFS0, dice_FT2, dice_FS0, signal-noise_t, variance explained, normalized variance explained, d_table_score. Kappa (kappa) and Rho (rho) were calculated as measures of TE-dependence and TE-independence, respectively. A t-test was performed between the distributions of T2*-model F-statistics associated with clusters (i.e., signal) and non-cluster voxels (i.e., noise) to generate a t-statistic (metric signal-noise_z) and p-value (metric signal-noise_p) measuring relative association of the component to signal over noise. The number of significant voxels not from clusters was calculated for each component. Next, component selection was performed to identify BOLD (TE-dependent), non-BOLD (TE-independent), and uncertain (low-variance) components using the Kundu decision tree (v2.5) \citep{kundu2013integrated}. This is based on the minimal criteria of the original MEICA decision tree without the more aggressive noise removal steps This workflow used numpy \citep{van2011numpy}, scipy \citep{virtanen2020scipy}, pandas \citep{mckinney2010data,reback2020pandas}, scikit-learn \citep{pedregosa2011scikit}, nilearn, bokeh \citep{bokehmanual}, matplotlib \citep{Hunter:2007}, and nibabel \citep{brett_matthew_2019_3233118}. This workflow also used the Dice similarity index \citep{dice1945measures,sorensen1948method}.
References
@Manual{bokehmanual,
title = {Bokeh: Python library for interactive visualization},
author = {{Bokeh Development Team}},
year = {2018},
url = {https://bokeh.pydata.org/en/latest/},
}
@article{dice1945measures,
title={Measures of the amount of ecologic association between species},
author={Dice, Lee R},
journal={Ecology},
volume={26},
number={3},
pages={297--302},
year={1945},
publisher={JSTOR},
url={https://doi.org/10.2307/1932409},
doi={10.2307/1932409}
}
@article{dupre2021te,
title={TE-dependent analysis of multi-echo fMRI with* tedana},
author={DuPre, Elizabeth and Salo, Taylor and Ahmed, Zaki and Bandettini, Peter A and Bottenhorn, Katherine L and Caballero-Gaudes, C{\'e}sar and Dowdle, Logan T and Gonzalez-Castillo, Javier and Heunis, Stephan and Kundu, Prantik and others},
journal={Journal of Open Source Software},
volume={6},
number={66},
pages={3669},
year={2021},
url={https://doi.org/10.21105/joss.03669},
doi={10.21105/joss.03669}
}
@article{kundu2013integrated,
title={Integrated strategy for improving functional connectivity mapping using multiecho fMRI},
author={Kundu, Prantik and Brenowitz, Noah D and Voon, Valerie and Worbe, Yulia and V{\'e}rtes, Petra E and Inati, Souheil J and Saad, Ziad S and Bandettini, Peter A and Bullmore, Edward T},
journal={Proceedings of the National Academy of Sciences},
volume={110},
number={40},
pages={16187--16192},
year={2013},
publisher={National Acad Sciences},
url={https://doi.org/10.1073/pnas.1301725110},
doi={10.1073/pnas.1301725110}
}
@inproceedings{mckinney2010data,
title={Data structures for statistical computing in python},
author={McKinney, Wes and others},
booktitle={Proceedings of the 9th Python in Science Conference},
volume={445},
number={1},
pages={51--56},
year={2010},
organization={Austin, TX},
url={https://doi.org/10.25080/Majora-92bf1922-00a},
doi={10.25080/Majora-92bf1922-00a}
}
@article{pedregosa2011scikit,
title={Scikit-learn: Machine learning in Python},
author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
journal={the Journal of machine Learning research},
volume={12},
pages={2825--2830},
year={2011},
publisher={JMLR. org},
url={http://jmlr.org/papers/v12/pedregosa11a.html}
}
@article{posse1999enhancement,
title={Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging},
author={Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R and Kiselev, Valerij G},
journal={Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine},
volume={42},
number={1},
pages={87--97},
year={1999},
publisher={Wiley Online Library},
url={https://doi.org/10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
doi={10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O}
}
@software{reback2020pandas,
author = {The pandas development team},
title = {pandas-dev/pandas: Pandas},
month = feb,
year = 2020,
publisher = {Zenodo},
version = {latest},
doi = {10.5281/zenodo.3509134},
url = {https://doi.org/10.5281/zenodo.3509134}
}
@article{sorensen1948method,
title={A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons},
author={Sorensen, Th A},
journal={Biol. Skar.},
volume={5},
pages={1--34},
year={1948}
}
@article{van2011numpy,
title={The NumPy array: a structure for efficient numerical computation},
author={Van Der Walt, Stefan and Colbert, S Chris and Varoquaux, Gael},
journal={Computing in science \& engineering},
volume={13},
number={2},
pages={22--30},
year={2011},
publisher={IEEE},
url={https://doi.org/10.1109/MCSE.2011.37},
doi={10.1109/MCSE.2011.37}
}
@article{virtanen2020scipy,
title={SciPy 1.0: fundamental algorithms for scientific computing in Python},
author={Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others},
journal={Nature methods},
volume={17},
number={3},
pages={261--272},
year={2020},
publisher={Nature Publishing Group},
url={https://doi.org/10.1038/s41592-019-0686-2},
doi={10.1038/s41592-019-0686-2}
}
