Summary

Anatomical

Anatomical Conformation

Brain mask and brain tissue segmentation of the T1w

This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.

Get figure file: sub-01/figures/sub-01_dseg.svg

Spatial normalization of the anatomical T1w reference

Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.

Spatial normalization of the T1w image to the MNI152NLin2009cAsym template.

Problem loading figure sub-01/figures/sub-01_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.
Get figure file: sub-01/figures/sub-01_space-MNI152NLin2009cAsym_T1w.svg

Arterial Spin Labelling

Reports for: task restEyesOpen.

Summary

Alignment of asl and anatomical MRI data (surface driven)

FSL flirt was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL fast (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.

Problem loading figure sub-01/figures/sub-01_task-restEyesOpen_desc-flirtbbr_asl.svg. If the link below works, please try reloading the report in your browser.
Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-flirtbbr_asl.svg

ASL Summary

Summary statistics are plotted, which may reveal trends or artifacts in the asl data. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point. A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.

Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-carpetplot_asl.svg

CBF Summary

This carpet plot shows the time series for all voxels within the brain mask for CBF. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green), white matter and CSF (red), indicated by the color map on the left-hand side. The score Index with value greater than zero indicates which volume(s) are removed by SCORE.

Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-cbftsplot_asl.svg

CBF maps

The maps plot cerebral blood flow (CBF) for basic CBF. The unit is mL 100/g/min

Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-cbfplot_asl.svg

SCORE CBF maps

The maps plot cerebral blood flow (CBF) for SCORE-corrected CBF. The unit is mL 100/g/min

Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-scoreplot_asl.svg

SCRUB CBF maps

The maps plot cerebral blood flow (CBF) for SCRUB-corrected CBF. The unit is mL 100/g/min

Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-scrubplot_asl.svg

BASIL CBF maps

The maps plot cerebral blood flow (CBF) for BASIL-estimated CBF. The unit is mL 100/g/min

Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-basilplot_asl.svg

PVC CBF maps

The maps plot cerebral blood flow (CBF) for partial volume-corrected CBF. The unit is mL 100/g/min

Get figure file: sub-01/figures/sub-01_task-restEyesOpen_desc-pvcplot_asl.svg

About

Methods

We kindly ask to report results preprocessed with this tool using the following boilerplate.

Results included in this manuscript come from preprocessing performed using aslprep 0.2.3, which is based on Nipype 1.5.0 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502).

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 (Tustison et al. 2010), distributed with ANTs 2.3.1 (Avants et al. 2008, 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 6.0.3:b862cdd5, RRID:SCR_002823, Zhang, Brady, and Smith 2001). Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with antsRegistration (ANTs 2.3.1), 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 [Fonov et al. (2009), RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],

Functional data preprocessing

For each of the 1 ASL 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 6.0.3:b862cdd5, Jenkinson et al. 2002). The BOLD reference was then co-registered to the T1w reference using flirt (FSL 6.0.3:b862cdd5, Jenkinson and Smith 2001) with the boundary-based registration (Greve and Fischl 2009) cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The CBF was quantified from preproccessed ASL data using a relatively basic model (Detre et al. 1992) (Alsop et al. 2015). CBF are susceptible to artifacts due to low signal to noise ratio and sensitivity to motion, Structural Correlation based Outlier Rejection (SCORE) algothim was applied to the CBF to discard few extreme outliers (Dolui et al. 2017). Furthermore,Structural Correlation with RobUst Bayesian (SCRUB) algorithms was applied to the CBF by iteratively reweighted CBF with structural tissues probalility maps (Sudipto Dolui David A. Wolk and Detre 2016). Alternate method of CBF computation is Bayesian Inference for Arterial Spin Labeling (BASIL) as implmented in FSL which is based on Bayeisan inference principles (Chappell et al. 2009). BASIL computed the CBF from ASL incoporating natural varaibility of other model parameters and spatial regularization of the estimated perfusion image. BASIL also included correction for partial volume effects (Chappell et al. 2011). The ASL and CBF dreivatives were resampled into standard space, generating a preprocessed ASL and computed CBF in MNI152NLin2009cAsym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Several confounding time-series were calculated based on the preprocessed ASL: framewise displacement (FD) and DVARS. FD and DVARS are calculated for each ASL run, both using their implementations in Nipype (following the definitions by Power et al. 2014). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file.

The following quality control (qc) measures was estimated: framewise displacement and relative root mean square dice index. Other qc meaure include dice and jaccard indices, cross-correlation and coverage that estimate the coregistration quality of ASL and T1W images and normalization quality of ASL to template. Quality evaluation index (QEI) was also computed for CBF (S. A. N. Sudipto Dolui Ronald Wolf 2016). The QEI is automated for objective quality evaluation of CBF maps and measured the CBF quality based on structural similarity,spatial variability and the percentatge of voxels with negtaive CBF within Grey matter 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 1964). Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer).

Many internal operations of aslprep use Nilearn 0.6.2 (Abraham et al. 2014, RRID:SCR_001362), mostly within the functional processing workflow. For more details of the pipeline, see the section corresponding to workflows in aslprep’s documentation.

The above boilerplate text was automatically generated by aslprep with the express intention that users should copy and paste this text into their manuscripts unchanged. It is released under the CC0 license.

References

Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” Frontiers in Neuroinformatics 8. https://doi.org/10.3389/fninf.2014.00014.

Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” Medical Image Analysis 12 (1): 26–41. https://doi.org/10.1016/j.media.2007.06.004.

Chappell, M. A., A. R. Groves, B. J. MacIntosh, M. J. Donahue, P. Jezzard, and M. W. Woolrich. 2011. “Partial Volume Correction of Multiple Inversion Time Arterial Spin Labeling MRI Data.” Magnetic Resonance in Medicine 65 (4). https://doi.org/10.1002/mrm.22641.

Chappell, Michael A., Adrian R. Groves, Brandon Whitcher, and Mark W. Woolrich. 2009. “Variational Bayesian Inference for a Nonlinear Forward Model.” IEEE Transactions on Signal Processing 57 (1). https://doi.org/10.1109/TSP.2008.2005752.

Detre, John A., John S. Leigh, Donald S. Williams, and Alan P. Koretsky. 1992. “Perfusion Imaging.” Magnetic Resonance in Medicine 23 (1). https://doi.org/10.1002/mrm.1910230106.

Dolui, Sudipto, Ze Wang, Russell T. Shinohara, David A. Wolk, John A. Detre, and for the Alzheimer’s Disease Neuroimaging Initiative. 2017. “Structural Correlation-Based Outlier Rejection (SCORE) Algorithm for Arterial Spin Labeling Time Series: SCORE: Denoising Algorithm for ASL.” Journal of Magnetic Resonance Imaging 45 (6). https://doi.org/10.1002/jmri.25436.

Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” NeuroImage 47, Supplement 1: S102. https://doi.org/10.1016/S1053-8119(09)70884-5.

Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” Frontiers in Neuroinformatics 5: 13. https://doi.org/10.3389/fninf.2011.00013.

Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” Software. Zenodo. https://doi.org/10.5281/zenodo.596855.

Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” NeuroImage 48 (1): 63–72. https://doi.org/10.1016/j.neuroimage.2009.06.060.

Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” NeuroImage 17 (2): 825–41. https://doi.org/10.1006/nimg.2002.1132.

Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” Medical Image Analysis 5 (2): 143–56. https://doi.org/10.1016/S1361-8415(01)00036-6.

Lanczos, C. 1964. “Evaluation of Noisy Data.” Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis 1 (1): 76–85. https://doi.org/10.1137/0701007.

Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” NeuroImage 84 (Supplement C): 320–41. https://doi.org/10.1016/j.neuroimage.2013.08.048.

Sudipto Dolui, David A. Wolk, and John A. Detre. 2016. “SCRUB: A Structural Correlation and Empirical Robust Bayesian Method for Asl Data.” International Society for Magnetic Resonance in Medicine, no. 1. https://doi.org/http://archive.ismrm.org/2016/2880.html.

Sudipto Dolui, Seyed Ali Nabavizadeh, Ronald Wolf. 2016. “Automated Quality Evaluation Index for 2D Asl Cbf Maps.” International Society for Magnetic Resonance in Medicine, no. 1. https://doi.org/http://indexsmart.mirasmart.com/ISMRM2017/PDFfiles/0682.html.

Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” IEEE Transactions on Medical Imaging 29 (6): 1310–20. https://doi.org/10.1109/TMI.2010.2046908.

Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” IEEE Transactions on Medical Imaging 20 (1): 45–57. https://doi.org/10.1109/42.906424.

Results included in this manuscript come from preprocessing
performed using *aslprep* 0.2.3,
which is based on *Nipype* 1.5.0
(@nipype1; @nipype2; RRID:SCR_002502).

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 2.3.1 [@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 6.0.3:b862cdd5, RRID:SCR_002823,
@fsl_fast].
Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
nonlinear registration with `antsRegistration` (ANTs 2.3.1),
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 1 ASL 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 6.0.3:b862cdd5, @mcflirt].
The BOLD reference was then co-registered to the T1w reference using
`flirt` [FSL 6.0.3:b862cdd5, @flirt] with the boundary-based registration [@bbr]
cost-function.
Co-registration was configured with nine degrees of freedom to account
for distortions remaining in the BOLD reference.
The BOLD time-series (including slice-timing correction when applied)
were resampled onto their original, native space by applying
the transforms to correct for head-motion.
These resampled BOLD time-series will be referred to as *preprocessed
BOLD in original space*, or just *preprocessed BOLD*.
The CBF was quantified from  *preproccessed* ASL data using a relatively basic
model [@detre_perfusion] [@alsop_recommended]. CBF are susceptible to artifacts
due to low signal to noise ratio  and  sensitivity to  motion, Structural Correlation
based Outlier Rejection (SCORE) algothim was applied to the CBF to discard few extreme
outliers [@score_dolui]. Furthermore,Structural Correlation with RobUst Bayesian (SCRUB)
algorithms was applied to the CBF by iteratively reweighted  CBF  with structural tissues
probalility maps [@scrub_dolui].  Alternate method of CBF computation is Bayesian Inference
for Arterial Spin Labeling (BASIL) as implmented in FSL which is  based on Bayeisan inference
principles [@chappell_basil]. BASIL computed the CBF from ASL incoporating natural varaibility
of other model parameters and spatial regularization of the estimated perfusion image. BASIL
also included correction for partial volume effects [@chappell_pvc].
The ASL and CBF dreivatives  were resampled into standard space,
generating a *preprocessed ASL and computed CBF in MNI152NLin2009cAsym space*.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
Several confounding time-series were calculated based on the
*preprocessed ASL*: framewise displacement (FD) and DVARS. 
FD and DVARS are calculated for each ASL run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.

The following quality control (qc) measures was estimated: framewise displacement and relative
root mean square dice index. Other qc meaure include dice and jaccard indices, cross-correlation
and coverage that estimate the coregistration quality of  ASL and T1W images and  normalization
quality of ASL to template. Quality evaluation index (QEI) was also computed for CBF [@cbfqc].
The  QEI is  automated for objective quality evaluation of CBF maps and measured the CBF quality
based on structural similarity,spatial variability and the percentatge  of voxels with  negtaive
CBF within Grey matter
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 *aslprep* use
*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *aslprep*'s documentation](https://aslprep.readthedocs.io/en/latest/workflows.html "aslprep's documentation").


### Copyright Waiver

The above boilerplate text was automatically generated by aslprep
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 \emph{aslprep} 0.2.3, which is based on \emph{Nipype} 1.5.0
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).

\begin{description}
\item[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 \texttt{N4BiasFieldCorrection} \citep{n4},
distributed with ANTs 2.3.1 \citep[RRID:SCR\_004757]{ants}, and used as
T1w-reference throughout the workflow. The T1w-reference was then
skull-stripped with a \emph{Nipype} implementation of the
\texttt{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 \texttt{fast} \citep[FSL 6.0.3:b862cdd5,
RRID:SCR\_002823,][]{fsl_fast}. Volume-based spatial normalization to
one standard space (MNI152NLin2009cAsym) was performed through nonlinear
registration with \texttt{antsRegistration} (ANTs 2.3.1), using
brain-extracted versions of both T1w reference and the T1w template. The
following template was selected for spatial normalization: \emph{ICBM
152 Nonlinear Asymmetrical template version 2009c}
{[}\citet{mni152nlin2009casym}, RRID:SCR\_008796; TemplateFlow ID:
MNI152NLin2009cAsym{]},
\item[Functional data preprocessing]
For each of the 1 ASL 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 \emph{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 \texttt{mcflirt} \citep[FSL
6.0.3:b862cdd5,][]{mcflirt}. The BOLD reference was then co-registered
to the T1w reference using \texttt{flirt} \citep[FSL
6.0.3:b862cdd5,][]{flirt} with the boundary-based registration
\citep{bbr} cost-function. Co-registration was configured with nine
degrees of freedom to account for distortions remaining in the BOLD
reference. The BOLD time-series (including slice-timing correction when
applied) were resampled onto their original, native space by applying
the transforms to correct for head-motion. These resampled BOLD
time-series will be referred to as \emph{preprocessed BOLD in original
space}, or just \emph{preprocessed BOLD}. The CBF was quantified from
\emph{preproccessed} ASL data using a relatively basic model
\citep{detre_perfusion} \citep{alsop_recommended}. CBF are susceptible
to artifacts due to low signal to noise ratio and sensitivity to motion,
Structural Correlation based Outlier Rejection (SCORE) algothim was
applied to the CBF to discard few extreme outliers \citep{score_dolui}.
Furthermore,Structural Correlation with RobUst Bayesian (SCRUB)
algorithms was applied to the CBF by iteratively reweighted CBF with
structural tissues probalility maps \citep{scrub_dolui}. Alternate
method of CBF computation is Bayesian Inference for Arterial Spin
Labeling (BASIL) as implmented in FSL which is based on Bayeisan
inference principles \citep{chappell_basil}. BASIL computed the CBF from
ASL incoporating natural varaibility of other model parameters and
spatial regularization of the estimated perfusion image. BASIL also
included correction for partial volume effects \citep{chappell_pvc}. The
ASL and CBF dreivatives were resampled into standard space, generating a
\emph{preprocessed ASL and computed CBF in MNI152NLin2009cAsym space}.
First, a reference volume and its skull-stripped version were generated
using a custom methodology of \emph{fMRIPrep}. Several confounding
time-series were calculated based on the \emph{preprocessed ASL}:
framewise displacement (FD) and DVARS. FD and DVARS are calculated for
each ASL run, both using their implementations in \emph{Nipype}
\citep[following the definitions by][]{power_fd_dvars}. The head-motion
estimates calculated in the correction step were also placed within the
corresponding confounds file.
\end{description}

The following quality control (qc) measures was estimated: framewise
displacement and relative root mean square dice index. Other qc meaure
include dice and jaccard indices, cross-correlation and coverage that
estimate the coregistration quality of ASL and T1W images and
normalization quality of ASL to template. Quality evaluation index (QEI)
was also computed for CBF \citep{cbfqc}. The QEI is automated for
objective quality evaluation of CBF maps and measured the CBF quality
based on structural similarity,spatial variability and the percentatge
of voxels with negtaive CBF within Grey matter All resamplings can be
performed with \emph{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 \texttt{antsApplyTransforms} (ANTs),
configured with Lanczos interpolation to minimize the smoothing effects
of other kernels \citep{lanczos}. Non-gridded (surface) resamplings were
performed using \texttt{mri\_vol2surf} (FreeSurfer).

Many internal operations of \emph{aslprep} use \emph{Nilearn} 0.6.2
\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
processing workflow. For more details of the pipeline, see
\href{https://aslprep.readthedocs.io/en/latest/workflows.html}{the
section corresponding to workflows in \emph{aslprep}'s documentation}.

\hypertarget{copyright-waiver}{%
\subsubsection{Copyright Waiver}\label{copyright-waiver}}

The above boilerplate text was automatically generated by aslprep with
the express intention that users should copy and paste this text into
their manuscripts \emph{unchanged}. It is released under the
\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.

\hypertarget{references}{%
\subsubsection{References}\label{references}}

\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/aslprep/data/boilerplate.bib}

Bibliography

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    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
    year = {2018},
    doi = {10.1038/s41592-018-0235-4},
    journal = {Nature Methods}
}

@article{fmriprep2,
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    title = {fMRIPrep},
    year = 2018,
    doi = {10.5281/zenodo.852659},
    publisher = {Zenodo},
    journal = {Software}
}

@article{nipype1,
    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
    doi = {10.3389/fninf.2011.00013},
    journal = {Frontiers in Neuroinformatics},
    pages = 13,
    shorttitle = {Nipype},
    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
    volume = 5,
    year = 2011
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