Outputs of tedana¶
tedana derivatives¶
Filename |
Content |
---|---|
t2sv.nii.gz |
Limited estimated T2* 3D map. Values are in seconds. The difference between the limited and full maps is that, for voxels affected by dropout where only one echo contains good data, the full map uses the single echo’s value while the limited map has a NaN. |
s0v.nii.gz |
Limited S0 3D map. The difference between the limited and full maps is that, for voxels affected by dropout where only one echo contains good data, the full map uses the single echo’s value while the limited map has a NaN. |
ts_OC.nii.gz |
Optimally combined time series. |
dn_ts_OC.nii.gz |
Denoised optimally combined time series. Recommended dataset for analysis. |
lowk_ts_OC.nii.gz |
Combined time series from rejected components. |
midk_ts_OC.nii.gz |
Combined time series from “mid-k” rejected components. |
hik_ts_OC.nii.gz |
High-kappa time series. This dataset does not include thermal noise or low variance components. Not the recommended dataset for analysis. |
adaptive_mask.nii.gz |
Integer-valued mask used in the workflow, where each voxel’s value corresponds to the number of good echoes to be used for T2*/S0 estimation. |
pca_decomposition.json |
TEDPCA component table. A BIDS Derivatives-compatible
json file with summary metrics and inclusion/exclusion
information for each component from the PCA
decomposition. To view, you may want to use
|
pca_mixing.tsv |
Mixing matrix (component time series) from PCA decomposition in a tab-delimited file. Each column is a different component, and the column name is the component number. |
pca_components.nii.gz |
Component weight maps from PCA decomposition. Each map corresponds to the same component index in the mixing matrix and component table. |
ica_decomposition.json |
TEDICA component table. A BIDS Derivatives-compatible
json file with summary metrics and inclusion/exclusion
information for each component from the ICA
decomposition. To view, you may want to use
|
ica_mixing.tsv |
Mixing matrix (component time series) from ICA decomposition in a tab-delimited file. Each column is a different component, and the column name is the component number. |
ica_components.nii.gz |
Component weight maps from ICA decomposition. Values are z-transformed standardized regression coefficients. Each map corresponds to the same component index in the mixing matrix and component table. Should be the same as “feats_OC2.nii.gz”. |
betas_OC.nii.gz |
Full ICA coefficient feature set. |
betas_hik_OC.nii.gz |
High-kappa ICA coefficient feature set |
feats_OC2.nii.gz |
Z-normalized spatial component maps |
report.txt |
A summary report for the workflow with relevant citations. |
If verbose
is set to True:
Filename |
Content |
---|---|
t2svG.nii.gz |
Full T2* map/time series. Values are in seconds. The difference between the limited and full maps is that, for voxels affected by dropout where only one echo contains good data, the full map uses the single echo’s value while the limited map has a NaN. Only used for optimal combination. |
s0vG.nii.gz |
Full S0 map/time series. Only used for optimal combination. |
hik_ts_e[echo].nii.gz |
High-Kappa time series for echo number |
midk_ts_e[echo].nii.gz |
Mid-Kappa time series for echo number |
lowk_ts_e[echo].nii.gz |
Low-Kappa time series for echo number |
dn_ts_e[echo].nii.gz |
Denoised time series for echo number |
If gscontrol
includes ‘gsr’:
Filename |
Content |
---|---|
T1gs.nii.gz |
Spatial global signal |
glsig.1D |
Time series of global signal from optimally combined data. |
tsoc_orig.nii.gz |
Optimally combined time series with global signal retained. |
tsoc_nogs.nii.gz |
Optimally combined time series with global signal removed. |
If gscontrol
includes ‘t1c’:
Filename |
Content |
---|---|
sphis_hik.nii.gz |
T1-like effect |
hik_ts_OC_T1c.nii.gz |
T1 corrected high-kappa time series by regression |
dn_ts_OC_T1c.nii.gz |
T1 corrected denoised time series |
betas_hik_OC_T1c.nii.gz |
T1-GS corrected high-kappa components |
meica_mix_T1c.1D |
T1-GS corrected mixing matrix |
Component tables¶
TEDPCA and TEDICA use tab-delimited tables to track relevant metrics, component classifications, and rationales behind classifications. TEDPCA rationale codes start with a “P”, while TEDICA codes start with an “I”.
Classification |
Description |
---|---|
accepted |
BOLD-like components included in denoised and high-Kappa data |
rejected |
Non-BOLD components excluded from denoised and high-Kappa data |
ignored |
Low-variance components included in denoised, but excluded from high-Kappa data |
TEDPCA codes¶
Code |
Classification |
Description |
---|---|---|
P001 |
rejected |
Low Rho, Kappa, and variance explained |
P002 |
rejected |
Low variance explained |
P003 |
rejected |
Kappa equals fmax |
P004 |
rejected |
Rho equals fmax |
P005 |
rejected |
Cumulative variance explained above 95% (only in stabilized PCA decision tree) |
P006 |
rejected |
Kappa below fmin (only in stabilized PCA decision tree) |
P007 |
rejected |
Rho below fmin (only in stabilized PCA decision tree) |
TEDICA codes¶
Code |
Classification |
Description |
---|---|---|
I001 |
rejected|accepted |
Manual classification |
I002 |
rejected |
Rho greater than Kappa |
I003 |
rejected |
More significant voxels in S0 model than R2 model |
I004 |
rejected |
S0 Dice is higher than R2 Dice and high variance explained |
I005 |
rejected |
Noise F-value is higher than signal F-value and high variance explained |
I006 |
ignored |
No good components found |
I007 |
rejected |
Mid-Kappa component |
I008 |
ignored |
Low variance explained |
I009 |
rejected |
Mid-Kappa artifact type A |
I010 |
rejected |
Mid-Kappa artifact type B |
I011 |
ignored |
ign_add0 |
I012 |
ignored |
ign_add1 |
Citable workflow summaries¶
tedana
generates a report for the workflow, customized based on the parameters used and including relevant citations.
The report is saved in a plain-text file, report.txt, in the output directory.
An example report
TE-dependence analysis was performed on input data. 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 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 ‘t2s’ (Posse et al., 1999) combination method. Global signal regression was applied to the multi-echo and optimally combined datasets. Principal component analysis followed by the Kundu component selection decision tree (Kundu et al., 2013) was applied to the optimally combined data for dimensionality reduction. Independent component analysis was then used to decompose the dimensionally reduced dataset. A series of TE-dependence metrics were calculated for each ICA component, including Kappa, Rho, and variance explained. 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; Kundu et al., 2013). T1c global signal regression was then applied to the data in order to remove spatially diffuse noise.
This workflow used numpy (Van Der Walt, Colbert, & Varoquaux, 2011), scipy (Jones et al., 2001), pandas (McKinney, 2010), scikit-learn (Pedregosa et al., 2011), nilearn, and nibabel (Brett et al., 2019).
This workflow also used the Dice similarity index (Dice, 1945; Sørensen, 1948).
References
Brett, M., Markiewicz, C. J., Hanke, M., Côté, M.-A., Cipollini, B., McCarthy, P., … freec84. (2019, May 28). nipy/nibabel. Zenodo. http://doi.org/10.5281/zenodo.3233118
Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297-302.
Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific Tools for Python, 2001-, http://www.scipy.org/
Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J., … & 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.
McKinney, W. (2010, June). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51-56).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
Posse, S., Wiese, S., Gembris, D., Mathiak, K., Kessler, C., Grosse‐Ruyken, M. L., … & Kiselev, V. G. (1999). Enhancement of BOLD‐contrast sensitivity by single‐shot multi‐echo functional MR imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 42(1), 87-97.
Sørensen, T. J. (1948). 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. I kommission hos E. Munksgaard.
Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), 22.