Outputs of fMRIPost-AROMA

fMRIPost-AROMA outputs conform to the BIDS Derivatives specification (see BIDS Derivatives, along with the upcoming BEP 011 and BEP 012). fMRIPost-AROMA generates three broad classes of outcomes:

Visual QA (quality assessment) reports: One HTML per subject, that allows the user a thorough visual assessment of the quality of processing and ensures the transparency of fMRIPost-AROMA operation.
ICA outputs: Outputs from the independent component analysis (ICA). For example, the mixing matrix and component weight maps.
Derivatives (denoised data): Denoised fMRI data in the requested output spaces and resolutions.
Confounds: Time series of ICA components classified as noise.

Layout

Assuming fMRIPost-AROMA is invoked with:

fmripost_aroma <input_dir>/ <output_dir>/ participant [OPTIONS]

The outputs will be a BIDS Derivatives dataset of the form:

<output_dir>/
  logs/
  sub-<label>/
  sub-<label>.html
  dataset_description.json
  .bidsignore

For each participant in the dataset, a directory of derivatives (sub-<label>/) and a visual report (sub-<label>.html) are generated. The log directory contains citation boilerplate text. dataset_description.json is a metadata file in which fMRIPost-AROMA records metadata recommended by the BIDS standard.

Visual Reports

fMRIPost-AROMA outputs summary reports, written to <output dir>/fmripost_aroma/sub-<label>.html. These reports provide a quick way to make visual inspection of the results easy.

Derivatives of fMRIPost-AROMA (denoised data)

Derivative data are written to <output dir>/sub-<label>/. The BIDS Derivatives specification describes the naming and metadata conventions we follow.

ICA derivatives

ICA outputs are stored in the func/ subfolder:

sub-<label>/
  func/
    sub-<label>_space-MNI152NLin6Asym_res-2_desc-melodic_mixing.tsv
    sub-<label>_space-MNI152NLin6Asym_res-2_desc-melodic_mixing.json
    sub-<label>_space-MNI152NLin6Asym_res-2_desc-melodic_components.nii.gz
    sub-<label>_space-MNI152NLin6Asym_res-2_desc-melodic_components.json

Functional derivatives

Functional derivatives are stored in the func/ subfolder. All derivatives contain task-<task_label> (mandatory) and run-<run_index> (optional), and these will be indicated with [specifiers]:

sub-<label>/
  func/
    sub-<label>_[specifiers]_space-MNI152NLin6Asym_res-2_desc-aggrDenoised_bold.nii.gz
    sub-<label>_[specifiers]_space-MNI152NLin6Asym_res-2_desc-nonaggrDenoised_bold.nii.gz
    sub-<label>_[specifiers]_space-MNI152NLin6Asym_res-2_desc-orthaggrDenoised_bold.nii.gz

Regularly gridded outputs (images). Volumetric output spaces labels (<label> above, and in the following) include MNI152NLin6Asym (default).

Extracted confounding time series. For each BOLD run processed with fMRIPost-AROMA, an accompanying confounds file will be generated. Confounds are saved as a TSV file:

sub-<label>/
  func/
    sub-<label>_[specifiers]_desc-aroma_metrics.tsv
    sub-<label>_[specifiers]_desc-aroma_metrics.json
    sub-<label>_[specifiers]_desc-aroma_timeseries.tsv
    sub-<label>_[specifiers]_desc-aroma_timeseries.json

Confounds

fMRIPost-AROMA outputs a set of confounds that can be used to denoise the data. These are stored in a TSV file (desc-aroma_timeseries.tsv) and a JSON file (desc-aroma_timeseries.json) that contains metadata about the confounds.

The confounds generated by fMRIPost-AROMA are ICA component time series classified as “rejected” by ICA-AROMA.

Columns starting with aroma_motion_ are the raw noise ICA component time series. Columns starting with aroma_orth_motion_ are the noise ICA component time series, after z-scoring and orthogonalization with respect to the signal ICA component time series.

Confounds and “carpet”-plot on the visual reports

The visual reports provide several sections per task and run to aid designing a denoising strategy for subsequent analysis. Some of the estimated confounds are plotted with a “carpet” visualization of the BOLD time series [Power2016]. An example of these plots follows:

See implementation on init_bold_confs_wf.