Usage¶
tedana minimally requires:
- acquired echo times (in milliseconds), and
- functional datasets equal to the number of acquired echoes.
But you can supply many other options, viewable with tedana -h
or
t2smap -h
.
For most use cases, we recommend that users call tedana from within existing fMRI preprocessing pipelines such as fMRIPrep or afni_proc.py.. Users can also construct their own preprocessing pipelines from which to call tedana; for recommendations on doing so, see our general guidelines for _constructing ME-EPI pipelines.
Run tedana¶
This is the full tedana workflow, which runs multi-echo ICA and outputs multi-echo denoised data along with many other derivatives. To see which files are generated by this workflow, check out the outputs page: https://tedana.readthedocs.io/en/latest/outputs.html
usage: tedana [-h] -d FILE [FILE ...] -e TE [TE ...] [--mask FILE]
[--mix FILE] [--ctab FILE] [--manacc MANACC] [--kdaw KDAW]
[--rdaw RDAW] [--conv CONV] [--sourceTEs STE]
[--combmode {t2s,ste}] [--cost {logcosh,cube,exp}]
[--denoiseTEs] [--strict] [--no_gscontrol] [--stabilize]
[--filecsdata] [--wvpca] [--label LABEL] [--seed FIXED_SEED]
Named Arguments¶
-d | Multi-echo dataset for analysis. May be a single file with spatially concatenated data or a set of echo-specific files, in the same order as the TEs are listed in the -e argument. |
-e | Echo times (in ms). E.g., 15.0 39.0 63.0 |
--mask | Binary mask of voxels to include in TE Dependent ANAlysis. Must be in the same space as data. |
--mix | File containing mixing matrix. If not provided, ME-PCA & ME-ICA is done. |
--ctab | File containing a component table from which to extract pre-computed classifications. |
--manacc | Comma separated list of manually accepted components |
--kdaw | Dimensionality augmentation weight (Kappa). Default=10. -1 for low-dimensional ICA Default: 10.0 |
--rdaw | Dimensionality augmentation weight (Rho). Default=1. -1 for low-dimensional ICA Default: 1.0 |
--conv | Convergence limit. Default 2.5e-5 Default: 2.5e-5 |
--sourceTEs | Source TEs for models. E.g., 0 for all, -1 for opt. com., and 1,2 for just TEs 1 and 2. Default=-1. Default: -1 |
--combmode | Possible choices: t2s, ste Combination scheme for TEs: t2s (Posse 1999, default), ste (Poser) Default: “t2s” |
--cost | Possible choices: logcosh, cube, exp Cost func. for ICA: logcosh (default), cube, exp Default: “logcosh” |
--denoiseTEs | Denoise each TE dataset separately. Default: False |
--strict | Ignore low-variance ambiguous components Default: False |
--no_gscontrol | Disable global signal regression. Default: True |
--stabilize | Stabilize convergence by reducing dimensionality, for low quality data Default: False |
--filecsdata | Save component selection data Default: False |
--wvpca | Perform PCA on wavelet-transformed data Default: False |
--label | Label for output directory. |
--seed | Value passed to repr(mdp.numx_rand.seed()) Set to an integer value for reproducible ICA results; otherwise, set to -1 for varying results across calls. Default: 42 |
Note
The --mask
argument is not intended for use with very conservative region-of-interest
analyses. One of the ways by which components are assessed as BOLD or non-BOLD is their
spatial pattern, so overly conservative masks will invalidate several steps in the tedana
workflow. To examine regions-of-interest with multi-echo data, apply masks after TE
Dependent ANAlysis.
Run t2smap¶
This workflow uses multi-echo data to optimally combine data across echoes and
to estimate T2* and S0 maps or time series.
To see which files are generated by this workflow, check out the workflow
documentation: tedana.workflows.t2smap_workflow()
.
usage: t2smap [-h] -d FILE [FILE ...] -e TE [TE ...] [--mask FILE]
[--fitmode {all,ts}] [--combmode {t2s,ste}] [--label LABEL]
Named Arguments¶
-d | Multi-echo dataset for analysis. May be a single file with spatially concatenated data or a set of echo-specific files, in the same order as the TEs are listed in the -e argument. |
-e | Echo times (in ms). E.g., 15.0 39.0 63.0 |
--mask | Binary mask of voxels to include in TE Dependent ANAlysis. Must be in the same space as data. |
--fitmode | Possible choices: all, ts Monoexponential model fitting scheme. “all” means that the model is fit, per voxel, across all timepoints. “ts” means that the model is fit, per voxel and per timepoint. Default: “all” |
--combmode | Possible choices: t2s, ste Combination scheme for TEs: t2s (Posse 1999, default), ste (Poser) Default: “t2s” |
--label | Label for output directory. |
Constructing ME-EPI pipelines¶
tedana
must be called in the context of a larger ME-EPI preprocessing pipeline.
Two common pipelines which support ME-EPI processing include fMRIPrep and afni_proc.py.
Users can also construct their own preprocessing pipeline for ME-EPI data from which to call tedana
.
There are several general principles to keep in mind when constructing ME-EPI processing pipelines.
In general, we recommend
- Performing slice timing correction and motion correction before
tedana
, and
2. Performing distortion correction, spatial normalization, smoothing,
and any rescaling or filtering after tedana
.
Other suggestions follow below.
3. Calculating slice time correction for ME-EPI¶
Similarly to single-echo EPI data, slice time correction allows us to assume that voxels across
slices represent roughly simultaneous events.
If the TR is slow enough to necessitate slice-timing (i.e., TR >= 1 sec., as a rule of thumb), then
slice-timing correction should be done before tedana
.
This is because slice timing differences may impact echo-dependent estimates.
The slice time is generally defined as the excitation pulse time for each slice. For single-echo EPI data, that excitation time would be the same regardless of the echo time, and the same is true when one is collecting multiple echoes after a single excitation pulse. Therefore, we suggest using the same slice timing for all echoes in an ME-EPI series.
4. Avoid applying individual transformations to each echo¶
When preparing ME-EPI data for multi-echo denoising as in tedana
, it is important
not to do anything that mean shifts the data or otherwise separately
scales the voxelwise values at each echo.
For example, head-motion correction parameters should not be calculated and applied at an individual echo level. Instead, we reccommend that researchers apply the same transforms to all echoes in an ME-EPI series. That is, that they calculate head motion correction parameters from one echo and apply the resulting transformation to all echoes.
Similarly, any intensity normalization or nuisance regressors should be applied to the data
after tedana
calculates the BOLD and non-BOLD weighting of components.
If this is not considered, resulting intensity gradients (e.g., in the case of scaling)
or alignment parameters (e.g., in the case of motion correction, normalization)
are likely to differ across echos,
and the subsequent calculation of voxelwise T2* values will be distorted.
See the description of tedana
’s ‘:doc: approach <approach> for more details
on how T2* values are calculated.