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docs: reformat abbreviations #386

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8 changes: 4 additions & 4 deletions docs/outputs.md
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# Outputs of *NiBabies*

*NiBabies* outputs conform to the :abbr:`BIDS (brain imaging data structure)`
*NiBabies* outputs conform to the {abbr}`BIDS (brain imaging data structure)`
Derivatives specification (see `BIDS Derivatives`_, along with the
upcoming `BEP 011`_ and `BEP 012`_).
*NiBabies* generates three broad classes of outcomes:
Expand Down Expand Up @@ -460,7 +460,7 @@ of possible confounds, which enable researchers to choose the most suitable deno
strategy for their downstream analyses.

Confounding variables calculated in *NiBabies* are stored separately for each subject,
session and run in :abbr:`TSV (tab-separated value)` files - one column for each confound variable.
session and run in {abbr}`TSV (tab-separated value)` files - one column for each confound variable.
Such tabular files may include over 100 columns of potential confound regressors.

:::{danger}
Expand Down Expand Up @@ -677,7 +677,7 @@ The procedure essentially follows the initial proposal of the approach by Patria
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
:abbr:`BOLD (blood-oxygen level-dependent)` time series [^Power2016].
{abbr}`BOLD (blood-oxygen level-dependent)` time series [^Power2016].

Noise components computed during each CompCor decomposition are evaluated according
to the fraction of variance that they explain across the nuisance ROI.
Expand All @@ -693,7 +693,7 @@ Also included is a plot of correlations among confound regressors.
This can be used to guide selection of a confound model or to assess the extent
to which tissue-specific regressors correlate with global signal.

See implementation on :mod:`~nibabies.workflows.bold.confounds.init_bold_confs_wf`.
See implementation on <python#nibabies.workflows.bold.confounds.init_bold_confs_wf>.

## References

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