Usage

tedana minimally requires:

  1. Acquired echo times (in milliseconds)
  2. 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. fMRIPrep currently supports Optimal combination through tedana, but not the full multi-echo denoising pipeline, although there are plans underway to integrate it. 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]
              [--sourceTEs SOURCE_TES] [--combmode {t2s}] [--verbose]
              [--tedort] [--gscontrol {t1c,gsr} [{t1c,gsr} ...]]
              [--tedpca {mle,kundu,kundu-stabilize}] [--out-dir OUT_DIR]
              [--seed FIXED_SEED] [--png] [--png-cmap PNG_CMAP]
              [--maxit MAXIT] [--maxrestart MAXRESTART] [--lowmem]

required 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

Named Arguments

--mask Binary mask of voxels to include in TE Dependent ANAlysis. Must be in the same space as data. If an explicit mask is not provided, then Nilearn’s compute_epi_mask function will be used to derive a mask from the first echo’s 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
--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

Combination scheme for TEs: t2s (Posse 1999, default)

Default: “t2s”

--verbose

Generate intermediate and additional files.

Default: False

--tedort

Orthogonalize rejected components w.r.t. accepted components prior to denoising.

Default: False

--gscontrol

Possible choices: t1c, gsr

Perform additional denoising to remove spatially diffuse noise. Default is None. This argument can be single value or a space delimited list

--tedpca

Possible choices: mle, kundu, kundu-stabilize

Method with which to select components in TEDPCA

Default: “mle”

--out-dir

Output directory.

Default: “.”

--seed

Value used for random initialization of ICA algorithm. Set to an integer value for reproducible ICA results. Set to -1 for varying results across ICA calls. Default=42.

Default: 42

--png

Creates a figures folder with static component maps, timecourse plots and other diagnostic images

Default: False

--png-cmap

Colormap for figures

Default: “coolwarm”

--maxit

Maximum number of iterations for ICA.

Default: 500

--maxrestart

Maximum number of attempts for ICA. If ICA fails to converge, the fixed seed will be updated and ICA will be run again. If convergence is achieved before maxrestart attempts, ICA will finish early.

Default: 10

--lowmem

Enables low-memory processing, including the use of IncrementalPCA. May increase workflow duration.

Default: False

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,paid}] [--label LABEL]

required 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

Named Arguments

--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, paid

Combination scheme for TEs: t2s (Posse 1999, default), paid (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

1. Perform slice timing correction and motion correction before tedana

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.

2. Perform distortion correction, spatial normalization, smoothing, and any rescaling or filtering after tedana

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 recommend 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 approach for more details on how T2* values are calculated.

Support and communication

All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/ME-ICA/tedana/issues.

If you would like to ask a question about usage or tedana’s outputs, please submit a question to NeuroStars with the multi-echo tag.

All previous tedana-related questions are available under the multi-echo tag.

We will also attempt to archive certain common questions and associate answers in the Frequently Asked Questions (FAQ) section below.

FAQ

ICA has failed to converge.

The TEDICA step may fail to converge if TEDPCA is either too strict (i.e., there are too few components) or too lenient (there are too many).

In our experience, this may happen when preprocessing has not been applied to the data, or when improper steps have been applied to the data (e.g., distortion correction, rescaling, nuisance regression). If you are confident that your data have been preprocessed correctly prior to applying tedana, and you encounter this problem, please submit a question to NeuroStars.

I think that some BOLD ICA components have been misclassified as noise.

tedana allows users to manually specify accepted components when calling the pipeline. You can use the --manacc argument to specify the indices of components to accept.

Why isn’t v3.2 of the component selection algorithm supported in tedana?

There is a lot of solid logic behind the updated version of the TEDICA component selection algorithm, first added to the original ME-ICA codebase here by Dr. Prantik Kundu. However, we (the tedana developers) have encountered certain difficulties with this method (e.g., misclassified components) and the method itself has yet to be validated in any papers, posters, etc., which is why we have chosen to archive the v3.2 code, with the goal of revisiting it when tedana is more stable.

Anyone interested in using v3.2 may compile and install an earlier release (<=0.0.4) of tedana.