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_1992] [@alsop_recommended_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 [@dolui2017structural]. Furthermore,Structural Correlation with RobUst Bayesian (SCRUB)
algorithms was applied to the CBF by iteratively reweighted  CBF  with structural tissues
probalility maps [@dolui2016scrub].  Alternate method of CBF computation is Bayesian Inference
for Arterial Spin Labeling (BASIL) as implmented in FSL which is  based on Bayeisan inference
principles [@chappell2008variational]. 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 [@dolui2017automated].
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_1992} \citep{alsop_recommended_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 \citep{dolui2017structural}.
Furthermore,Structural Correlation with RobUst Bayesian (SCRUB)
algorithms was applied to the CBF by iteratively reweighted CBF with
structural tissues probalility maps \citep{dolui2016scrub}. 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{chappell2008variational}. 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{dolui2017automated}. 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

@article{fmriprep1,
    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
    year = {2018},
    doi = {10.1038/s41592-018-0235-4},
    journal = {Nature Methods}
}

@article{fmriprep2,
    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
    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
}

@article{nipype2,
    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
    title = {Nipype},
    year = 2018,
    doi = {10.5281/zenodo.596855},
    publisher = {Zenodo},
    journal = {Software}
}

@article{n4,
    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
    doi = {10.1109/TMI.2010.2046908},
    issn = {0278-0062},
    journal = {IEEE Transactions on Medical Imaging},
    number = 6,
    pages = {1310-1320},
    shorttitle = {N4ITK},
    title = {N4ITK: Improved N3 Bias Correction},
    volume = 29,
    year = 2010
}

@article{fs_reconall,
    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
    doi = {10.1006/nimg.1998.0395},
    issn = {1053-8119},
    journal = {NeuroImage},
    number = 2,
    pages = {179-194},
    shorttitle = {Cortical Surface-Based Analysis},
    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
    volume = 9,
    year = 1999
}



@article{mindboggle,
    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
    doi = {10.1371/journal.pcbi.1005350},
    issn = {1553-7358},
    journal = {PLOS Computational Biology},
    number = 2,
    pages = {e1005350},
    title = {Mindboggling morphometry of human brains},
    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
    volume = 13,
    year = 2017
}

@article{mni152lin,
    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
    volume = {2},
    issn = {1053-8119},
    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
    doi = {10.1006/nimg.1995.1012},
    number = {2, Part A},
    journal = {NeuroImage},
    year = {1995},
    pages = {89--101}
}

@article{mni152nlin2009casym,
    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
    doi = {10.1016/S1053-8119(09)70884-5},
    journal = {NeuroImage},
    pages = {S102},
    volume = {47, Supplement 1},
    year = 2009
}

@article{mni152nlin6asym,
    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
    title = {Brain templates and atlases},
    doi = {10.1016/j.neuroimage.2012.01.024},
    journal = {NeuroImage},
    volume = {62},
    number = {2},
    pages = {911--922},
    year = 2012
}

@article{ants,
    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
    doi = {10.1016/j.media.2007.06.004},
    issn = {1361-8415},
    journal = {Medical Image Analysis},
    number = 1,
    pages = {26-41},
    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
    volume = 12,
    year = 2008
}

@article{fsl_fast,
    author = {Zhang, Y. and Brady, M. and Smith, S.},
    doi = {10.1109/42.906424},
    issn = {0278-0062},
    journal = {IEEE Transactions on Medical Imaging},
    number = 1,
    pages = {45-57},
    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
    volume = 20,
    year = 2001
}


@article{fieldmapless1,
    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
    doi = {10.3389/fninf.2017.00017},
    issn = {1662-5196},
    journal = {Frontiers in Neuroinformatics},
    language = {English},
    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
    volume = 11,
    year = 2017
}

@phdthesis{fieldmapless2,
    address = {Berlin},
    author = {Huntenburg, Julia M.},
    language = {eng},
    school = {Freie Universität},
    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
    type = {Master's Thesis},
    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
    year = 2014
}

@article{fieldmapless3,
    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
    doi = {10.1371/journal.pone.0152472},
    issn = {1932-6203},
    journal = {PLOS ONE},
    number = 3,
    pages = {e0152472},
    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
    volume = 11,
    year = 2016
}

@article{flirt,
    title = {A global optimisation method for robust affine registration of brain images},
    volume = {5},
    issn = {1361-8415},
    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
    doi = {10.1016/S1361-8415(01)00036-6},
    number = {2},
    urldate = {2018-07-27},
    journal = {Medical Image Analysis},
    author = {Jenkinson, Mark and Smith, Stephen},
    year = {2001},
    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
    pages = {143--156}
}

@article{mcflirt,
    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
    doi = {10.1006/nimg.2002.1132},
    issn = {1053-8119},
    journal = {NeuroImage},
    number = 2,
    pages = {825-841},
    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
    volume = 17,
    year = 2002
}

@article{bbr,
    author = {Greve, Douglas N and Fischl, Bruce},
    doi = {10.1016/j.neuroimage.2009.06.060},
    issn = {1095-9572},
    journal = {NeuroImage},
    number = 1,
    pages = {63-72},
    title = {Accurate and robust brain image alignment using boundary-based registration},
    volume = 48,
    year = 2009
}

@article{aroma,
    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
    doi = {10.1016/j.neuroimage.2015.02.064},
    issn = {1053-8119},
    journal = {NeuroImage},
    number = {Supplement C},
    pages = {267-277},
    shorttitle = {ICA-AROMA},
    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
    volume = 112,
    year = 2015
}

@article{power_fd_dvars,
    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
    doi = {10.1016/j.neuroimage.2013.08.048},
    issn = {1053-8119},
    journal = {NeuroImage},
    number = {Supplement C},
    pages = {320-341},
    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
    volume = 84,
    year = 2014
}

@article{confounds_satterthwaite_2013,
    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
    doi = {10.1016/j.neuroimage.2012.08.052},
    issn = {10538119},
    journal = {NeuroImage},
    number = 1,
    pages = {240--256},
    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
    volume = 64,
    year = 2013
}


@article{nilearn,
    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
    doi = {10.3389/fninf.2014.00014},
    issn = {1662-5196},
    journal = {Frontiers in Neuroinformatics},
    language = {English},
    title = {Machine learning for neuroimaging with scikit-learn},
    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
    volume = 8,
    year = 2014
}

@article{lanczos,
    author = {Lanczos, C.},
    doi = {10.1137/0701007},
    issn = {0887-459X},
    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
    number = 1,
    pages = {76-85},
    title = {Evaluation of Noisy Data},
    url = {http://epubs.siam.org/doi/10.1137/0701007},
    volume = 1,
    year = 1964
}

@article{compcor,
    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
    doi = {10.1016/j.neuroimage.2007.04.042},
    issn = {1053-8119},
    journal = {NeuroImage},
    number = 1,
    pages = {90-101},
    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
    volume = 37,
    year = 2007
}

@article{hcppipelines,
    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
    doi = {10.1016/j.neuroimage.2013.04.127},
    issn = {1053-8119},
    journal = {NeuroImage},
    pages = {105-124},
    series = {Mapping the Connectome},
    title = {The minimal preprocessing pipelines for the Human Connectome Project},
    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
    volume = 80,
    year = 2013
}

@article{fs_template,
    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
    doi = {10.1016/j.neuroimage.2010.07.020},
    journal = {NeuroImage},
    number = 4,
    pages = {1181-1196},
    title = {Highly accurate inverse consistent registration: A robust approach},
    volume = 53,
    year = 2010
}

@article{afni,
    author = {Cox, Robert W. and Hyde, James S.},
    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
    journal = {NMR in Biomedicine},
    number = {4-5},
    pages = {171-178},
    title = {Software tools for analysis and visualization of fMRI data},
    volume = 10,
    year = 1997
}

@article{posse_t2s,
    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
    journal = {Magnetic Resonance in Medicine},
    number = 1,
    pages = {87-97},
    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
    volume = 42,
    year = 1999
}


@article{dolui2017automated,
    author = {Sudipto Dolui, Ronald Wolf, Seyed Ali Nabavizadeh, David A. Wolk, John A. Detre},
    doi = {http://indexsmart.mirasmart.com/ISMRM2017/PDFfiles/0682.html},
    journal = {International Society for Magnetic Resonance in Medicine},
    number = 1,
    title = {Automated Quality Evaluation Index for 2D ASL CBF Maps},
    year = 2016
}


@article{dolui2017structural,
	title = {Structural {Correlation}-based {Outlier} {Rejection} ({SCORE}) algorithm for arterial spin labeling time series: {SCORE}: {Denoising} {Algorithm} for {ASL}},
	volume = 45,
	doi = {10.1002/jmri.25436},
	number = 6,
	journal = {Journal of Magnetic Resonance Imaging},
	author = {Dolui, Sudipto and Wang, Ze and Shinohara, Russell T. and Wolk, David A. and Detre, John A. and {for the Alzheimer's Disease Neuroimaging Initiative}},
	year = 2017
}


@article{dolui2016scrub,
    title = {SCRUB: A Structural Correlation and Empirical Robust Bayesian Method for ASL Data},
    doi = {http://archive.ismrm.org/2016/2880.html},
    journal = {International Society for Magnetic Resonance in Medicine},
    number = 1,
    author = {Sudipto Dolui, David A. Wolk, and John A. Detre},
    year = 2016
}

@article{chappell2008variational,
	title = {Variational Bayesian Inference for a Nonlinear Forward Model},
	volume = 57,
	doi = {10.1109/TSP.2008.2005752},
	number=1,
	journal = {IEEE Transactions on Signal Processing},
	author = {Chappell, Michael A. and Groves, Adrian R. and Whitcher, Brandon and Woolrich, Mark W.},
	year = 2009,
}

@article{chappell_pvc,
	title = {Partial volume correction of multiple inversion time arterial spin labeling {MRI} data},
	volume = 65,
	doi = {10.1002/mrm.22641},
	number = 4,
	journal = {Magnetic Resonance in Medicine},
	author = {Chappell, M. A. and Groves, A. R. and MacIntosh, B. J. and Donahue, M. J. and Jezzard, P. and Woolrich, M. W.},
	year = 2011
}


@article{detre_perfusion_1992,
	title = {Perfusion imaging},
	volume = 23,
	doi = {10.1002/mrm.1910230106},
	number = 1,
	journal = {Magnetic Resonance in Medicine},
	author = {Detre, John A. and Leigh, John S. and Williams, Donald S. and Koretsky, Alan P.},
	year = 1992
}

@article{alsop_recommended_2015,
	title = {Recommended Implementation of Arterial Spin Labeled Perfusion {MRI} for Clinical Applications: A consensus of the {ISMRM} Perfusion Study Group and the European Consortium for ASL in Dementia},
	doi = {10.1002/mrm.25197},
	number=1,
	journal = {Magnetic resonance in medicine},
	author = {Alsop, David C. and Detre, John A. and Golay, Xavier and Günther, Matthias and Hendrikse, Jeroen and Hernandez-Garcia, Luis and Lu, Hanzhang and MacIntosh, Bradley J. and Parkes, Laura M. and Smits, Marion and van Osch, Matthias J. P. and Wang, Danny JJ and Wong, Eric C. and Zaharchuk, Greg},
	year = 2015
}