Outputs of tedana¶
|t2sv.nii||Limited estimated T2* 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.|
|s0v.nii||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||Optimally combined time series.|
|dn_ts_OC.nii||Denoised optimally combined time series. Recommended dataset for analysis.|
|lowk_ts_OC.nii||Combined time series from rejected components.|
|midk_ts_OC.nii||Combined time series from “mid-k” rejected components.|
|hik_ts_OC.nii||High-kappa time series. This dataset does not include thermal noise or low variance components. Not the recommended dataset for analysis.|
|comp_table_pca.txt||TEDPCA component table. A tab-delimited file with summary metrics and inclusion/exclusion information for each component from the PCA decomposition.|
|mepca_mix.1D||Mixing matrix (component time series) from PCA decomposition.|
|meica_mix.1D||Mixing matrix (component time series) from ICA decomposition. The only differences between this mixing matrix and the one above are that components may be sorted differently and signs of time series may be flipped.|
|betas_OC.nii||Full ICA coefficient feature set.|
|betas_hik_OC.nii||High-kappa ICA coefficient feature set|
|feats_OC2.nii||Z-normalized spatial component maps|
|comp_table_ica.txt||TEDICA component table. A tab-delimited file with summary metrics and inclusion/exclusion information for each component from the ICA decomposition.|
verbose is set to True:
|t2ss.nii||Voxel-wise T2* estimates using ascending numbers of echoes, starting with 2.|
|s0vs.nii||Voxel-wise S0 estimates using ascending numbers of echoes, starting with 2.|
|t2svG.nii||Full T2* map/time series. 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||Full S0 map/time series. Only used for optimal combination.|
|__meica_mix.1D||Mixing matrix (component time series) from ICA decomposition.|
|hik_ts_e[echo].nii||High-Kappa time series for echo number
|midk_ts_e[echo].nii||Mid-Kappa time series for echo number
|lowk_ts_e[echo].nii||Low-Kappa time series for echo number
|dn_ts_e[echo].nii||Denoised time series for echo number
gscontrol includes ‘gsr’:
|T1gs.nii||Spatial global signal|
|glsig.1D||Time series of global signal from optimally combined data.|
|tsoc_orig.nii||Optimally combined time series with global signal retained.|
|tsoc_nogs.nii||Optimally combined time series with global signal removed.|
gscontrol includes ‘t1c’:
|hik_ts_OC_T1c.nii||T1 corrected high-kappa time series by regression|
|dn_ts_OC_T1c.nii||T1 corrected denoised time series|
|betas_hik_OC_T1c.nii||T1-GS corrected high-kappa components|
|meica_mix_T1c.1D||T1-GS corrected mixing matrix|
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”.
|accepted||BOLD-like components retained in denoised and high-Kappa data|
|rejected||Non-BOLD components removed from denoised and high-Kappa data|
|ignored||Low-variance components ignored in denoised, but not high-Kappa, data|
|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)|
|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|
|I008||ignored||Low variance explained|
|I009||rejected||Mid-Kappa artifact type A|
|I010||rejected||Mid-Kappa artifact type B|
Static visual reports can be generated by using the
--png flag when calling
tedana from the command line.
Images are created and placed within the output directory, in a folder labeled
These reports consist of three main types of images.
For each component identified by tedana, a single image will be created. Above is an example of an accepted component. These are designed for an up-close inspection of both the spatial and temporal aspects of the component, as well as ancillary information.
The title of the plot provides information about variance, kappa and rho values as well as the reasons for rejection, if any (see above for codes).
Below this is the component timeseries, color coded on the basis of its classification. Green for accepted, Red for rejected, Black for ignored or unclassified.
Slices are then selected from sagittal, axial and coronal planes, to highlight the component pattern. By default these images used the red-blue colormap and are scaled to 10% of the max beta value.
You can select your own colormap to use by specifying its name when calling
For example, to use the bone colormap, you would add
Finally, the bottom of the image shows the Fast Fourier Transform of the component timeseries.
Tip: Look for your fundamental task frequencies here!
Above, you can review a component that was rejected. In this case, the subject moved each time the task was performed - which affected single slices of the fMRI volume. This scan used multiband imaging (collecting multiple slices at once), so the motion artifact occurs in more than once slice.
Kappa vs Rho Scatter Plot¶
This diagnostic plot shows the relationship between kappa and rho values for each component.
This can be useful for getting a big picture view of your data or for comparing denoising performance with various fMRI sequences.
Double Pie Chart¶
This diagnostic plot shows the relative variance explained by each classification type in the outer ring, with individual components on the inner ring. If a low amount of variance is explained, this will be shown as a gap in the ring.
Tip: Sometimes large variance is due to singular components, which can be easily seen here.