Duplicated boilerplate in CITATION.md

[mateuszpawlik]

What happened?

After executing fmriprep, CITATION.md contains multiple versions of some sections. The references are missing. See Additional information.

We are guessing this is due to different slice timing parameters but that's just a guess.

Please let me know any other information you need.

What command did you use?

singularity run --cleanenv --bind /mnt/dbgroup-share/mpawlik/data-ccns/soc21:/data --bind /mnt/dbgroup-share/mpawlik/scratch/soc21:/work --bind /home/mpawlik/bids/freesurfer_license.txt:/opt/freesurfer/license.txt fmriprep_21.0.2.sif /data /data/derivatives/fmriprep participant --nprocs 4 --fs-no-reconall --notrack -v -w /work

What version of fMRIPrep are you running?

21.0.2

How are you running fMRIPrep?

Singularity

Is your data BIDS valid?

Yes

Are you reusing any previously computed results?

No

Please copy and paste any relevant log output.

No errors to report!

Additional information / screenshots

The content of CITATION.md:

Results included in this manuscript come from preprocessing
performed using *fMRIPrep* 21.0.0
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.6.1
(@nipype1; @nipype2; RRID:SCR_002502).



Preprocessing of B<sub>0</sub> inhomogeneity mappings

: A total of 1 fieldmaps were found available within the input
BIDS structure for this particular subject.
A *B<sub>0</sub>* nonuniformity map (or *fieldmap*) was estimated from the
phase-drift map(s) measure with two consecutive GRE (gradient-recalled echo)
acquisitions.
The corresponding phase-map(s) were phase-unwrapped with `prelude` (FSL 6.0.5.1:57b01774).

Anatomical data preprocessing

: A total of 1 T1-weighted (T1w) images were found within the input
BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.3.3 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
as target template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL 6.0.5.1:57b01774, RRID:SCR_002823,
@fsl_fast].
Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
nonlinear registration with `antsRegistration` (ANTs 2.3.3),
using brain-extracted versions of both T1w reference and the T1w template.
The following template was selected for spatial normalization:
*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].

Functional data preprocessing

: For each of the 2 BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
`mcflirt` [FSL 6.0.5.1:57b01774, @mcflirt].
The estimated *fieldmap* was then aligned with rigid-registration to the target
EPI (echo-planar imaging) reference run.
The field coefficients were mapped on to the reference EPI using the transform.
BOLD runs were slice-time corrected to 0.479s (0.5 of slice acquisition range
0s-0.958s) using `3dTshift` from AFNI  [@afni, RRID:SCR_005927].
The BOLD reference was then co-registered to the T1w reference using
`mri_coreg` (FreeSurfer) followed by `flirt` [FSL 6.0.5.1:57b01774, @flirt]
with the boundary-based registration [@bbr] cost-function.
Co-registration was configured with six degrees of freedom.
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
tCompCor components are then calculated from the top 2% variable
voxels within the brain mask.
For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
are generated in anatomical space.
The implementation differs from that of Behzadi et al. in that instead
of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by thresholding the corresponding partial volume map at 0.05, and it ensures components are not extracted
from voxels containing a minimal fraction of GM.
Finally, these masks are resampled into BOLD space and binarized by
thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of 0.5 mm FD or
1.5 standardised DVARS were annotated as motion outliers.
The BOLD time-series were resampled into standard space,
generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).


Many internal operations of *fMRIPrep* use
*Nilearn* 0.8.1 [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").


### Copyright Waiver

The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.

### References


Results included in this manuscript come from preprocessing
performed using *fMRIPrep* 21.0.0
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.6.1
(@nipype1; @nipype2; RRID:SCR_002502).



Preprocessing of B<sub>0</sub> inhomogeneity mappings

: A total of 1 fieldmaps were found available within the input
BIDS structure for this particular subject.
A *B<sub>0</sub>* nonuniformity map (or *fieldmap*) was estimated from the
phase-drift map(s) measure with two consecutive GRE (gradient-recalled echo)
acquisitions.
The corresponding phase-map(s) were phase-unwrapped with `prelude` (FSL 6.0.5.1:57b01774).

Anatomical data preprocessing

: A total of 1 T1-weighted (T1w) images were found within the input
BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.3.3 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
as target template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL 6.0.5.1:57b01774, RRID:SCR_002823,
@fsl_fast].
Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
nonlinear registration with `antsRegistration` (ANTs 2.3.3),
using brain-extracted versions of both T1w reference and the T1w template.
The following template was selected for spatial normalization:
*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].

Functional data preprocessing

: For each of the 2 BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
`mcflirt` [FSL 6.0.5.1:57b01774, @mcflirt].
The estimated *fieldmap* was then aligned with rigid-registration to the target
EPI (echo-planar imaging) reference run.
The field coefficients were mapped on to the reference EPI using the transform.
BOLD runs were slice-time corrected to 0.478s (0.5 of slice acquisition range
0s-0.955s) using `3dTshift` from AFNI  [@afni, RRID:SCR_005927].
The BOLD reference was then co-registered to the T1w reference using
`mri_coreg` (FreeSurfer) followed by `flirt` [FSL 6.0.5.1:57b01774, @flirt]
with the boundary-based registration [@bbr] cost-function.
Co-registration was configured with six degrees of freedom.
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
tCompCor components are then calculated from the top 2% variable
voxels within the brain mask.
For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
are generated in anatomical space.
The implementation differs from that of Behzadi et al. in that instead
of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by thresholding the corresponding partial volume map at 0.05, and it ensures components are not extracted
from voxels containing a minimal fraction of GM.
Finally, these masks are resampled into BOLD space and binarized by
thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of 0.5 mm FD or
1.5 standardised DVARS were annotated as motion outliers.
The BOLD time-series were resampled into standard space,
generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).


Many internal operations of *fMRIPrep* use
*Nilearn* 0.8.1 [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").


### Copyright Waiver

The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.

### References


Results included in this manuscript come from preprocessing
performed using *fMRIPrep* 21.0.0
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.6.1
(@nipype1; @nipype2; RRID:SCR_002502).



Preprocessing of B<sub>0</sub> inhomogeneity mappings

: A total of 1 fieldmaps were found available within the input
BIDS structure for this particular subject.
A *B<sub>0</sub>* nonuniformity map (or *fieldmap*) was estimated from the
phase-drift map(s) measure with two consecutive GRE (gradient-recalled echo)
acquisitions.
The corresponding phase-map(s) were phase-unwrapped with `prelude` (FSL 6.0.5.1:57b01774).

Anatomical data preprocessing

: A total of 1 T1-weighted (T1w) images were found within the input
BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.3.3 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
as target template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL 6.0.5.1:57b01774, RRID:SCR_002823,
@fsl_fast].
Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
nonlinear registration with `antsRegistration` (ANTs 2.3.3),
using brain-extracted versions of both T1w reference and the T1w template.
The following template was selected for spatial normalization:
*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].

Functional data preprocessing

: For each of the 2 BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
`mcflirt` [FSL 6.0.5.1:57b01774, @mcflirt].
The estimated *fieldmap* was then aligned with rigid-registration to the target
EPI (echo-planar imaging) reference run.
The field coefficients were mapped on to the reference EPI using the transform.
BOLD runs were slice-time corrected to 0.478s (0.5 of slice acquisition range
0s-0.955s) using `3dTshift` from AFNI  [@afni, RRID:SCR_005927].
The BOLD reference was then co-registered to the T1w reference using
`mri_coreg` (FreeSurfer) followed by `flirt` [FSL 6.0.5.1:57b01774, @flirt]
with the boundary-based registration [@bbr] cost-function.
Co-registration was configured with six degrees of freedom.
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
tCompCor components are then calculated from the top 2% variable
voxels within the brain mask.
For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
are generated in anatomical space.
The implementation differs from that of Behzadi et al. in that instead
of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by thresholding the corresponding partial volume map at 0.05, and it ensures components are not extracted
from voxels containing a minimal fraction of GM.
Finally, these masks are resampled into BOLD space and binarized by
thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of 0.5 mm FD or
1.5 standardised DVARS were annotated as motion outliers.
The BOLD time-series were resampled into standard space,
generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).

Functional data preprocessing

: For each of the 2 BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
`mcflirt` [FSL 6.0.5.1:57b01774, @mcflirt].
The estimated *fieldmap* was then aligned with rigid-registration to the target
EPI (echo-planar imaging) reference run.
The field coefficients were mapped on to the reference EPI using the transform.
BOLD runs were slice-time corrected to 0.479s (0.5 of slice acquisition range
0s-0.958s) using `3dTshift` from AFNI  [@afni, RRID:SCR_005927].
The BOLD reference was then co-registered to the T1w reference using
`mri_coreg` (FreeSurfer) followed by `flirt` [FSL 6.0.5.1:57b01774, @flirt]
with the boundary-based registration [@bbr] cost-function.
Co-registration was configured with six degrees of freedom.
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
tCompCor components are then calculated from the top 2% variable
voxels within the brain mask.
For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
are generated in anatomical space.
The implementation differs from that of Behzadi et al. in that instead
of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by thresholding the corresponding partial volume map at 0.05, and it ensures components are not extracted
from voxels containing a minimal fraction of GM.
Finally, these masks are resampled into BOLD space and binarized by
thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of 0.5 mm FD or
1.5 standardised DVARS were annotated as motion outliers.
The BOLD time-series were resampled into standard space,
generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).


Many internal operations of *fMRIPrep* use
*Nilearn* 0.8.1 [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").


### Copyright Waiver

The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.

### References

[effigies]

Thanks for the report!

References are expected to be missing from the markdown. They are intended to be included in the rendered HTML and we need a heading, and the generated .tex file will include the associated .bib file.

I'm not sure why you're getting the duplication; different parameters across runs seems plausible. A PR that resolves it would be welcome, but this will be low priority to fix ourselves, as users can use their own discretion to clip the parts they need.