### Arterial Spin-Labeled MRI Preprocessing and Cerebral Blood Flow Computation

Arterial spin-labeled MRI images were preprocessed using *ASLPrep* 0.2.8, 
which is based on *Nipype* 1.7.0 [@nipype].



### Anatomical data preprocessing
*sMRIPrep* 0.6.1 was used to process the anatomical data.
A total of 1 T1-weighted (T1w) image was found within the input
BIDS dataset. The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
with `N4BiasFieldCorrection` [@n4], which is distributed with *ANTs*  2.3.1 [@ants]. *sMRIPrep* uses this T1w reference throughout the workflow.
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow  using OASIS30ANTs
as  the target template. Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w reference image using *FSL*'s `FAST` [@fsl_fast].
Nonlinear registration of  the brain-extracted T1w reference image to the 
brain-extracted template was accomplished using  `antsRegistration`.
The following template  was selected for spatial normalization:
*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym], 

### ASL data preprocessing

For the 2 ASL runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
First, the middle volume of the ASL timeseries was selected as the refernce volume and 
brain extracted using *Nipype*'s custom brain extraction workflow.
Head-motion parameters were estimated using *FSL*’s `mcflirt` [ @mcflirt]. 
Next, ASLPrep wrote head-motion parameters to the ASL run’s confound file. 

Susceptibility distortion correction (SDC) was omitted.
ASLPrep co-registered the ASL reference to the T1w reference using *FSL*’s `flirt` [@flirt], which 
implemented the boundary-based registration cost-function [@bbr]. Co-registration used
6 degrees of freedom. The quality of co-registration and normalization to template was quantified 
using the Dice and Jaccard indices, the cross-correlation with the reference image, and the overlap between 
the ASL and reference images (e.g., image coverage). 
Several confounding timeseries were calculated, including both framewise displacement 
(FD) and DVARS. FD and DVARS are calculated using the implementations in in *Nipype*
(following the definition by [@power_fd_dvars]) for each ASL run.  ASLPrep summarizes 
in-scanner motion as the mean framewise displacement and relative root-mean square displacement.

### Cerebral blood flow computation and denoising

*ASLPrep* was configured to calculate cerebral blood flow (CBF) using the following methods. 

The cerebral blood flow (CBF) was quantified from  preprocessed ASL data using a general kinetic model
[@kinetic].


Structural Correlation based Outlier Rejection (SCORE) algorithm was applied to the CBF timeseries
to discard CBF volumes with outlying values [@score_dolui] before computing the mean CBF. 
Following SCORE, the Structural Correlation with RobUst Bayesian (SCRUB) algorithm was applied to the CBF maps
using structural tissue probability maps to reweight the mean CBF [@score_dolui;@scrub_dolui]. 


CBF was also computed with Bayesian Inference for Arterial Spin Labeling (BASIL) [@chappell_basil], 
as implemented in *FSL* 6.0.3. BASIL computes CBF using a spatial regularization of the estimated 
perfusion image and additionally calculates a partial-volume corrected CBF image [@chappell_pvc].
For each CBF map, the ROIs for the following atlases were extracted: the  Harvard-Oxford  and the Schaefer 200 and 400-parcel resolution atlases.

The Quality evaluation index (QEI) was computed for each CBF map [@cbfqc]. 
QEI is based on the similarity between the CBF and the structural images, the spatial 
variability of the CBF image, and the percentage of grey matter voxels containing 
negative CBF values.  
All resampling in *ASLPrep* uses a single interpolation step that concatenates all transformations. 
Gridded (volumetric) resampling was performed using `antsApplyTransforms`, configured with *Lanczos*
interpolation to minimize the smoothing effects of other kernels [@lanczos].
 Many internal operations of *ASLPrep* use
*Nilearn* 0.8.1 [@nilearn], *NumPy* [@numpy], and *SciPy* [@scipy]. 
For more details of the pipeline, see [the  *ASLPrep*  documentation.](https://aslprep.readthedocs.io/en/latest/workflows.html).


### Copyright Waiver

The above methods description  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 unchanged [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.

### References

\hypertarget{arterial-spin-labeled-mri-preprocessing-and-cerebral-blood-flow-computation}{%
\subsubsection{Arterial Spin-Labeled MRI Preprocessing and Cerebral
Blood Flow
Computation}\label{arterial-spin-labeled-mri-preprocessing-and-cerebral-blood-flow-computation}}

Arterial spin-labeled MRI images were preprocessed using \emph{ASLPrep}
0.2.8, which is based on \emph{Nipype} 1.7.0 \citep{nipype}.

\hypertarget{anatomical-data-preprocessing}{%
\subsubsection{Anatomical data
preprocessing}\label{anatomical-data-preprocessing}}

\emph{sMRIPrep} 0.6.1 was used to process the anatomical data. A total
of 1 T1-weighted (T1w) image was found within the input BIDS dataset.
The T1-weighted (T1w) image was corrected for intensity non-uniformity
(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, which is
distributed with \emph{ANTs} 2.3.1 \citep{ants}. \emph{sMRIPrep} uses
this T1w reference throughout the workflow. The T1w-reference was then
skull-stripped with a \emph{Nipype} implementation of the
\texttt{antsBrainExtraction.sh} workflow using OASIS30ANTs as the target
template. Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on the
brain-extracted T1w reference image using \emph{FSL}'s \texttt{FAST}
\citep{fsl_fast}. Nonlinear registration of the brain-extracted T1w
reference image to the brain-extracted template was accomplished using
\texttt{antsRegistration}. The following template was selected for
spatial normalization: \emph{ICBM 152 Nonlinear Asymmetrical template
version 2009c} \citep{mni152nlin2009casym},

\hypertarget{asl-data-preprocessing}{%
\subsubsection{ASL data preprocessing}\label{asl-data-preprocessing}}

For the 2 ASL runs found per subject (across all tasks and sessions),
the following preprocessing was performed. First, the middle volume of
the ASL timeseries was selected as the refernce volume and brain
extracted using \emph{Nipype}'s custom brain extraction workflow.
Head-motion parameters were estimated using \emph{FSL}'s
\texttt{mcflirt} \citep{mcflirt}. Next, ASLPrep wrote head-motion
parameters to the ASL run's confound file.

Susceptibility distortion correction (SDC) was omitted. ASLPrep
co-registered the ASL reference to the T1w reference using \emph{FSL}'s
\texttt{flirt} \citep{flirt}, which implemented the boundary-based
registration cost-function \citep{bbr}. Co-registration used 6 degrees
of freedom. The quality of co-registration and normalization to template
was quantified using the Dice and Jaccard indices, the cross-correlation
with the reference image, and the overlap between the ASL and reference
images (e.g., image coverage). Several confounding timeseries were
calculated, including both framewise displacement (FD) and DVARS. FD and
DVARS are calculated using the implementations in in \emph{Nipype}
(following the definition by \citep{power_fd_dvars}) for each ASL run.
ASLPrep summarizes in-scanner motion as the mean framewise displacement
and relative root-mean square displacement.

\hypertarget{cerebral-blood-flow-computation-and-denoising}{%
\subsubsection{Cerebral blood flow computation and
denoising}\label{cerebral-blood-flow-computation-and-denoising}}

\emph{ASLPrep} was configured to calculate cerebral blood flow (CBF)
using the following methods.

The cerebral blood flow (CBF) was quantified from preprocessed ASL data
using a general kinetic model \citep{kinetic}.

Structural Correlation based Outlier Rejection (SCORE) algorithm was
applied to the CBF timeseries to discard CBF volumes with outlying
values \citep{score_dolui} before computing the mean CBF. Following
SCORE, the Structural Correlation with RobUst Bayesian (SCRUB) algorithm
was applied to the CBF maps using structural tissue probability maps to
reweight the mean CBF \citep{score_dolui, scrub_dolui}.

CBF was also computed with Bayesian Inference for Arterial Spin Labeling
(BASIL) \citep{chappell_basil}, as implemented in \emph{FSL} 6.0.3.
BASIL computes CBF using a spatial regularization of the estimated
perfusion image and additionally calculates a partial-volume corrected
CBF image \citep{chappell_pvc}. For each CBF map, the ROIs for the
following atlases were extracted: the Harvard-Oxford and the Schaefer
200 and 400-parcel resolution atlases.

The Quality evaluation index (QEI) was computed for each CBF map
\citep{cbfqc}. QEI is based on the similarity between the CBF and the
structural images, the spatial variability of the CBF image, and the
percentage of grey matter voxels containing negative CBF values.\\
All resampling in \emph{ASLPrep} uses a single interpolation step that
concatenates all transformations. Gridded (volumetric) resampling was
performed using \texttt{antsApplyTransforms}, configured with
\emph{Lanczos} interpolation to minimize the smoothing effects of other
kernels \citep{lanczos}. Many internal operations of \emph{ASLPrep} use
\emph{Nilearn} 0.8.1 \citep{nilearn}, \emph{NumPy} \citep{numpy}, and
\emph{SciPy} \citep{scipy}. For more details of the pipeline, see
\href{https://aslprep.readthedocs.io/en/latest/workflows.html}{the
\emph{ASLPrep} documentation.}.

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

The above methods description was automatically generated by
\emph{ASLPrep} with the express intention that users should copy and
paste this text into their manuscripts unchanged. It is released under
the unchanged
\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{bbr,
	title = {Accurate and robust brain image alignment using boundary-based registration},
	volume = {48},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2009.06.060},
	abstract = {The fine spatial scales of the structures in the human brain represent an enormous challenge to the successful integration of information from different images for both within- and between-subject analysis. While many algorithms to register image pairs from the same subject exist, visual inspection shows that their accuracy and robustness to be suspect, particularly when there are strong intensity gradients and/or only part of the brain is imaged. This paper introduces a new algorithm called Boundary-Based Registration, or BBR. The novelty of BBR is that it treats the two images very differently. The reference image must be of sufficient resolution and quality to extract surfaces that separate tissue types. The input image is then aligned to the reference by maximizing the intensity gradient across tissue boundaries. Several lower quality images can be aligned through their alignment with the reference. Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial-brain images to whole-brain images, a domain in which existing registration algorithms frequently fail. Even in the limit of registering a single slice, we show the BBR results to be robust and accurate.},
	language = {eng},
	number = {1},
	journal = {NeuroImage},
	author = {Greve, Douglas N. and Fischl, Bruce},
	month = oct,
	year = {2009},
	pmid = {19573611},
	pmcid = {PMC2733527},
	keywords = {Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Reproducibility of Results},
	pages = {63--72},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/BRFJ4IKS/Greve and Fischl - 2009 - Accurate and robust brain image alignment using bo.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/U333GSCH/Greve and Fischl - 2009 - Accurate and robust brain image alignment using bo.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/ATZDZAWD/Greve and Fischl - 2009 - Accurate and robust brain image alignment using bo.pdf:application/pdf},
}

@article{kinetic,
	title = {A general kinetic model for quantitative perfusion imaging with arterial spin labeling},
	volume = {40},
	doi = {10.1002/mrm.1910400308},
	number = {3},
	journal = {Magn Reson Med.},
	author = {Buxton, R. B.  and  Frank, L. R.  and  Wong, E. C. and Siewert, B. and Warach, S. and Edelman, R. R.},
	month = sep,
	year = {1998},
	pmid = {9727941},
	pages = {383--396},
}


@article{mcflirt,
	title = {Improved optimization for the robust and accurate linear registration and motion correction of brain images},
	volume = {17},
	issn = {1053-8119},
	doi = {10.1016/s1053-8119(02)91132-8},
	abstract = {Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.},
	language = {eng},
	number = {2},
	journal = {NeuroImage},
	author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
	month = oct,
	year = {2002},
	pmid = {12377157},
	keywords = {Acoustic Stimulation, Algorithms, Brain, Computer Simulation, Data Interpretation, Statistical, Fuzzy Logic, Humans, Image Interpretation, Computer-Assisted, Linear Models, Models, Neurological, Motion, Photic Stimulation, Reproducibility of Results},
	pages = {825--841},
}

@article{ants,
	title = {Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain},
	volume = {12},
	issn = {1361-8423},
	shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
	doi = {10.1016/j.media.2007.06.004},
	abstract = {One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.},
	language = {eng},
	number = {1},
	journal = {Medical Image Analysis},
	author = {Avants, B. B. and Epstein, C. L. and Grossman, M. and Gee, J. C.},
	month = feb,
	year = {2008},
	pmid = {17659998},
	pmcid = {PMC2276735},
	keywords = {Algorithms, Atrophy, Cerebral Cortex, Dementia, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging},
	pages = {26--41},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/8KDBMBLT/Avants et al. - 2008 - Symmetric diffeomorphic image registration with cr.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/K68PFZXW/Avants et al. - 2008 - Symmetric diffeomorphic image registration with cr.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/L4AZDPLP/Avants et al. - 2008 - Symmetric diffeomorphic image registration with cr.pdf:application/pdf},
}

@article{n4,
	title = {{N4ITK}: improved {N3} bias correction},
	volume = {29},
	issn = {1558-254X},
	shorttitle = {{N4ITK}},
	doi = {10.1109/TMI.2010.2046908},
	abstract = {A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.},
	language = {eng},
	number = {6},
	journal = {IEEE transactions on medical imaging},
	author = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A. and Gee, James C.},
	month = jun,
	year = {2010},
	pmid = {20378467},
	pmcid = {PMC3071855},
	keywords = {Algorithms, Artifacts, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Reproducibility of Results, Sensitivity and Specificity},
	pages = {1310--1320},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/6CLXHYYP/Tustison et al. - 2010 - N4ITK improved N3 bias correction.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/92WXKYCK/Tustison et al. - 2010 - N4ITK improved N3 bias correction.pdf:application/pdf},
}

@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}},
	volume = {73},
	issn = {0740-3194},
	shorttitle = {Recommended {Implementation} of {Arterial} {Spin} {Labeled} {Perfusion} {MRI} for {Clinical} {Applications}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190138/},
	doi = {10.1002/mrm.25197},
	abstract = {This article provides a summary statement of recommended implementations of arterial spin labeling (ASL) for clinical applications. It is a consensus of the ISMRM Perfusion Study Group and the European ‘ASL in Dementia’ consortium, both of whom met to reach this consensus in October 2012 in Amsterdam. Although ASL continues to undergo rapid technical development, we believe that current ASL methods are robust and ready to provide useful clinical information, and that a consensus statement on recommended implementations will help the clinical community to adopt a standardized approach. In this article we describe the major considerations and tradeoffs in implementing an ASL protocol, and provide specific recommendations for a standard approach. Our conclusions are that, as an optimal default implementation we recommend: pseudo-continuous labeling, background suppression, a segmented 3D readout without vascular crushing gradients, and calculation and presentation of both label/control difference images and cerebral blood flow in absolute units using a simplified model.},
	number = {1},
	urldate = {2020-04-13},
	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},
	month = jan,
	year = {2015},
	pmid = {24715426},
	pmcid = {PMC4190138},
	keywords = {arterial spin labeling, Blood Flow Velocity, Brain, cerebral blood flow, Cerebrovascular Circulation, Dementia, European Union, Humans, Magnetic Resonance Angiography, Neurology, perfusion, Practice Guidelines as Topic, Spin Labels},
	pages = {102--116},
	file = {Full Text:/Users/adebimpe/Zotero/storage/WA4YXG7L/Alsop et al. - 2015 - Recommended implementation of arterial spin-labele.pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/NGE56CU2/Alsop et al. - 2015 - Recommended implementation of arterial spin-labele.pdf:application/pdf;Full Text PDF:/Users/adebimpe/Zotero/storage/JMNAH5SP/Alsop et al. - 2015 - Recommended implementation of arterial spin-labele.pdf:application/pdf;Full Text PDF:/Users/adebimpe/Zotero/storage/5EG9I2E6/Alsop et al. - 2015 - Recommended implementation of arterial spin-labele.pdf:application/pdf;Full Text PDF:/Users/adebimpe/Zotero/storage/K7PAYUTX/Alsop et al. - 2015 - Recommended implementation of arterial spin-labele.pdf:application/pdf;PubMed Central Full Text PDF:/Users/adebimpe/Zotero/storage/2Q2KZ2MZ/Alsop et al. - 2015 - Recommended Implementation of Arterial Spin Labele.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/6L4MMAEM/mrm.html:text/html;Snapshot:/Users/adebimpe/Zotero/storage/Q4T9II9S/mrm.html:text/html;Snapshot:/Users/adebimpe/Zotero/storage/GHHYBSNY/mrm.html:text/html},
}

@article{detre_perfusion_1992,
	title = {Perfusion imaging},
	volume = {23},
	copyright = {Copyright © 1992 Wiley‐Liss, Inc., A Wiley Company},
	issn = {1522-2594},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.1910230106},
	doi = {10.1002/mrm.1910230106},
	abstract = {Measurement of tissue perfusion is important for the functional assessment of organs in vivo. Here we report the use of 1H NMR imaging to generate perfusion maps in the rat brain at 4.7 T. Blood water flowing to the brain is saturated in the neck region with a sliceselective saturation imaging sequence, creating an endogenous tracer in the form of proximally saturated spins. Because proton T1 times are relatively long, particularly at high field strengths, saturated spins exchange with bulk water in the brain and a steady state is created where the regional concentration of saturated spins is determined by the regional blood flow and regional T1. Distal saturation applied equidistantly outside the brain serves as a control for effects of the saturation pulses. Average cerebral blood flow in normocapnic rat brain under halothane anesthesia was determined to be 105 ± 16 cc. 100 g−1. min−1 (mean ± SEM, n = 3), in good agreement with values reported in the literature, and was sensitive to increases in arterial pCO2. This technique allows regional perfusion maps to be measured noninvasively, with the resolution of 1H MRI, and should be readily applicable to human studies. © 1992 Academic Press, Inc.},
	language = {en},
	number = {1},
	urldate = {2020-04-13},
	journal = {Magnetic Resonance in Medicine},
	author = {Detre, John A. and Leigh, John S. and Williams, Donald S. and Koretsky, Alan P.},
	year = {1992},
	pages = {37--45},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/AZY3F2PL/Detre et al. - 1992 - Perfusion imaging.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/WPJVE3GZ/mrm.html:text/html;Snapshot:/Users/adebimpe/Zotero/storage/M4E8Y24C/mrm.html:text/html},
}

@article{chappell_partial_2011,
	title = {Partial volume correction of multiple inversion time arterial spin labeling {MRI} data},
	volume = {65},
	issn = {1522-2594},
	doi = {10.1002/mrm.22641},
	abstract = {The accuracy of cerebral blood flow (CBF) estimates from arterial spin labeling (ASL) is affected by the presence of both gray matter (GM) and white matter within any voxel. Recently a partial volume (PV) correction method for ASL has been demonstrated (Asllani et al. Magn Reson Med 2008; 60:1362-1371), where PV estimates were used with a local linear regression to separate the GM and white matter ASL signal. Here a new PV correction method for multi-inversion time ASL is proposed that exploits PV estimates within a spatially regularized kinetic curve model analysis. The proposed method exploits both PV estimates and the different kinetics of the ASL signal arising from GM and white matter. The new correction method is shown, on both simulated and real data, to provide correction of GM CBF comparable to a linear regression approach, whilst preserving greater spatial detail in the CBF image. On real data corrected GM CBF values were found to be largely independent of GM PV, implying that the correction had been successful. Increases of mean GM CBF after correction of 69-80\% were observed.},
	language = {eng},
	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.},
	month = apr,
	year = {2011},
	pmid = {21337417},
	keywords = {Artifacts, Cerebral Arteries, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Angiography, Reproducibility of Results, Sensitivity and Specificity, Spin Labels},
	pages = {1173--1183},
	file = {Full Text:/Users/adebimpe/Zotero/storage/IICEL9XE/Chappell et al. - 2011 - Partial volume correction of multiple inversion ti.pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/BPURB8L5/Chappell et al. - 2011 - Partial volume correction of multiple inversion ti.pdf:application/pdf},
}

@article{chappell_variational_2009,
	title = {Variational {Bayesian} {Inference} for a {Nonlinear} {Forward} {Model}},
	volume = {57},
	issn = {1941-0476},
	doi = {10.1109/TSP.2008.2005752},
	abstract = {Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior distributions for linear models, by providing a fast method for Bayesian inference by estimating the parameters of a factorized approximation to the posterior distribution. Here a VB method for nonlinear forward models with Gaussian additive noise is presented. In the case of noninformative priors the parameter estimates obtained from this VB approach are identical to those found via nonlinear least squares. However, the advantage of the VB method lies in its Bayesian formulation, which permits prior information to be included in a hierarchical structure and measures of uncertainty for all parameter estimates to be obtained via the posterior distribution. Unlike other Bayesian methods VB is only approximate in comparison with the sampling method of MCMC. However, the VB method is found to be comparable and the assumptions made about the form of the posterior distribution reasonable. Practically, the VB approach is substantially faster than MCMC as fewer calculations are required. Some of the advantages of the fully Bayesian nature of the method are demonstrated through the extension of the noise model and the inclusion of automatic relevance determination (ARD) within the VB algorithm.},
	number = {1},
	journal = {IEEE Transactions on Signal Processing},
	author = {Chappell, Michael A. and Groves, Adrian R. and Whitcher, Brandon and Woolrich, Mark W.},
	month = jan,
	year = {2009},
	keywords = {Additive noise, approximation theory, automatic relevance determination, Bayesian methods, belief networks, Biomedical signal processing, Context modeling, Councils, factorized approximation, Gaussian additive noise, Inference algorithms, inference mechanisms, Least squares approximation, magnetic resonance imaging, Magnetic resonance imaging, nonlinear estimation, nonlinear forward model, nonlinear least squares, parameter estimation, Parameter estimation, posterior distributions, Sampling methods, signal processing, Signal processing algorithms, variational Bayesian inference},
	pages = {223--236},
	file = {IEEE Xplore Abstract Record:/Users/adebimpe/Zotero/storage/XVV2KUID/4625948.html:text/html;IEEE Xplore Abstract Record:/Users/adebimpe/Zotero/storage/2IZZGQJK/4625948.html:text/html;IEEE Xplore Full Text PDF:/Users/adebimpe/Zotero/storage/2PM8WXQ4/Chappell et al. - 2009 - Variational Bayesian Inference for a Nonlinear For.pdf:application/pdf},
}

@article{score_dolui,
	title = {Structural {Correlation}-based {Outlier} {Rejection} ({SCORE}) algorithm for arterial spin labeling time series: {SCORE}: {Denoising} {Algorithm} for {ASL}},
	volume = {45},
	issn = {10531807},
	shorttitle = {Structural {Correlation}-based {Outlier} {Rejection} ({SCORE}) algorithm for arterial spin labeling time series},
	url = {http://doi.wiley.com/10.1002/jmri.25436},
	doi = {10.1002/jmri.25436},
	language = {en},
	number = {6},
	urldate = {2020-04-13},
	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}},
	month = jun,
	year = {2017},
	keywords = {ADNI, Aged, Algorithms, Alzheimer Disease, Alzheimer's disease, arterial spin labeling, Artifacts, Brain, cerebral blood flow, Cerebrovascular Circulation, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Angiography, Male, outlier rejection, Reproducibility of Results, Sensitivity and Specificity, Spin Labels},
	pages = {1786--1797},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/28WLPTXK/Dolui et al. - 2017 - Structural Correlation-based Outlier Rejection (SC.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/QK82SC8R/Dolui et al. - 2017 - Structural Correlation-based Outlier Rejection (SC.pdf:application/pdf},
}

@misc{noauthor_ismrm_nodate,
	title = {({ISMRM} 2017) {Automated} {Quality} {Evaluation} {Index} for {2D} {ASL} {CBF} {Maps}},
	url = {http://archive.ismrm.org/2017/0682.html},
	urldate = {2020-04-13},
}

@article{nipype,
	title = {Nipype: {A} {Flexible}, {Lightweight} and {Extensible} {Neuroimaging} {Data} {Processing} {Framework} in {Python}},
	volume = {5},
	issn = {1662-5196},
	shorttitle = {Nipype},
	url = {https://www.frontiersin.org/articles/10.3389/fninf.2011.00013/full},
	doi = {10.3389/fninf.2011.00013},
	abstract = {Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient and optimal use of neuroimaging analysis approaches: 1) No uniform access to neuroimaging analysis software and usage information; 2) No framework for comparative algorithm development and dissemination; 3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; 4) Neuroimaging software packages do not address computational efficiency; and 5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is BSD licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.},
	language = {English},
	urldate = {2020-12-15},
	journal = {Frontiers in Neuroinformatics},
	author = {Gorgolewski, Krzysztof and Burns, Christopher D. and Madison, Cindee and Clark, Dav and Halchenko, Yaroslav O. and Waskom, Michael L. and Ghosh, Satrajit S.},
	year = {2011},
	note = {Publisher: Frontiers},
	keywords = {data processing, neuroimaging, Neuroimaging, pipeline, python, Python, reproducible research, Reproducible Research, workflow},
	file = {Full Text:/Users/adebimpe/Zotero/storage/IAF7G66B/Gorgolewski et al. - 2011 - Nipype a flexible, lightweight and extensible neu.pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/HWIG35V6/Gorgolewski et al. - 2011 - Nipype A Flexible, Lightweight and Extensible Neu.pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/YEFHGW6P/Gorgolewski et al. - 2011 - Nipype A Flexible, Lightweight and Extensible Neu.pdf:application/pdf;Full Text PDF:/Users/adebimpe/Zotero/storage/UUK4BQPM/Gorgolewski et al. - 2011 - Nipype A Flexible, Lightweight and Extensible Neu.pdf:application/pdf},
}

@article{fs_template,
	title = {Highly accurate inverse consistent registration: a robust approach},
	volume = {53},
	issn = {1095-9572},
	shorttitle = {Highly accurate inverse consistent registration},
	doi = {10.1016/j.neuroimage.2010.07.020},
	abstract = {The registration of images is a task that is at the core of many applications in computer vision. In computational neuroimaging where the automated segmentation of brain structures is frequently used to quantify change, a highly accurate registration is necessary for motion correction of images taken in the same session, or across time in longitudinal studies where changes in the images can be expected. This paper, inspired by Nestares and Heeger (2000), presents a method based on robust statistics to register images in the presence of differences, such as jaw movement, differential MR distortions and true anatomical change. The approach we present guarantees inverse consistency (symmetry), can deal with different intensity scales and automatically estimates a sensitivity parameter to detect outlier regions in the images. The resulting registrations are highly accurate due to their ability to ignore outlier regions and show superior robustness with respect to noise, to intensity scaling and outliers when compared to state-of-the-art registration tools such as FLIRT (in FSL) or the coregistration tool in SPM.},
	language = {eng},
	number = {4},
	journal = {NeuroImage},
	author = {Reuter, Martin and Rosas, H. Diana and Fischl, Bruce},
	month = dec,
	year = {2010},
	pmid = {20637289},
	pmcid = {PMC2946852},
	keywords = {Algorithms, Brain, Humans, Image Interpretation, Computer-Assisted, Models, Theoretical},
	pages = {1181--1196},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/2YXXI78Z/Reuter et al. - 2010 - Highly accurate inverse consistent registration a.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/CSEPTJW5/Reuter et al. - 2010 - Highly accurate inverse consistent registration a.pdf:application/pdf},
}

@article{fsl_fast,
	title = {Segmentation of brain {MR} images through a hidden {Markov} random field model and the expectation-maximization algorithm},
	volume = {20},
	issn = {0278-0062},
	doi = {10.1109/42.906424},
	abstract = {The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation--no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.},
	language = {eng},
	number = {1},
	journal = {IEEE transactions on medical imaging},
	author = {Zhang, Y. and Brady, M. and Smith, S.},
	month = jan,
	year = {2001},
	pmid = {11293691},
	keywords = {Algorithms, Brain, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Markov Chains},
	pages = {45--57},
}

@article{afni,
	title = {Software tools for analysis and visualization of {fMRI} data},
	volume = {10},
	issn = {0952-3480},
	doi = {10.1002/(sici)1099-1492(199706/08)10:4/5<171::aid-nbm453>3.0.co;2-l},
	abstract = {The tools needed for analysis and visualization of three-dimensional human brain functional magnetic resonance image results are outlined, covering the processing categories of data storage, interactive vs batch mode operations, visualization, spatial normalization (Talairach coordinates, etc.), analysis of functional activation, integration of multiple datasets, and interface standards. One freely available software package is described in some detail. The features and scope that a generally useful and extensible fMRI toolset should have are contrasted with what is available today. The article ends with a discussion of how the fMRI research community can cooperate to create standards and develop software that meets the community's needs.},
	language = {eng},
	number = {4-5},
	journal = {NMR in biomedicine},
	author = {Cox, R. W. and Hyde, J. S.},
	month = aug,
	year = {1997},
	pmid = {9430344},
	keywords = {Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Oxygen, Software},
	pages = {171--178},
}

@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},
	abstract = {Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework — one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage.},
	language = {en},
	number = {2},
	urldate = {2020-12-16},
	journal = {Medical Image Analysis},
	author = {Jenkinson, Mark and Smith, Stephen},
	month = jun,
	year = {2001},
	keywords = {Affine transformation, Brain Mapping, Costs and Cost Analysis, Global optimisation, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Mathematics, Multi-resolution search, Multimodal registration, Robustness},
	pages = {143--156},
	file = {ScienceDirect Full Text PDF:/Users/adebimpe/Zotero/storage/KT7L9WYX/Jenkinson and Smith - 2001 - A global optimisation method for robust affine reg.pdf:application/pdf;ScienceDirect Snapshot:/Users/adebimpe/Zotero/storage/DFFAAF2P/S1361841501000366.html:text/html},
}

@article{mni152nlin2009casym,
	title = {Unbiased {Average} {Age}-{Appropriate} {Atlases} for {Pediatric} {Studies}},
	volume = {54},
	issn = {1053-8119},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2962759/},
	doi = {10.1016/j.neuroimage.2010.07.033},
	abstract = {Spatial normalization, registration, and segmentation techniques for Magnetic Resonance Imaging (MRI) often use a target or template volume to facilitate processing, take advantage of prior information, and define a common coordinate system for analysis. In the neuroimaging literature, the MNI305 Talairach-like coordinate system is often used as a standard template. However, when studying pediatric populations, variation from the adult brain makes the MNI305 suboptimal for processing brain images of children. Morphological changes occurring during development render the use of age-appropriate templates desirable to reduce potential errors and minimize bias during processing of pediatric data. This paper presents the methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5–18.5 years, while maintaining a high level of anatomical detail and contrast. The creation of anatomical T1-weighted, T2-weighted, and proton density-weighted templates for specific developmentally important age-ranges, used data derived from the largest epidemiological, representative (healthy and normal) sample of the U.S. population, where each subject was carefully screened for medical and psychiatric factors and characterized using established neuropsychological and behavioral assessments. . Use of these age-specific templates was evaluated by computing average tissue maps for gray matter, white matter, and cerebrospinal fluid for each specific age range, and by conducting an exemplar voxel-wise deformation-based morphometry study using 66 young (4.5–6.9 years) participants to demonstrate the benefits of using the age-appropriate templates. The public availability of these atlases/templates will facilitate analysis of pediatric MRI data and enable comparison of results between studies in a common standardized space specific to pediatric research.},
	number = {1},
	urldate = {2021-07-23},
	journal = {NeuroImage},
	author = {Fonov, Vladimir and Evans, Alan C. and Botteron, Kelly and Almli, C. Robert and McKinstry, Robert C. and Collins, D. Louis},
	month = jan,
	year = {2011},
	pmid = {20656036},
	pmcid = {PMC2962759},
	pages = {313--327},
	file = {PubMed Central Full Text PDF:/Users/adebimpe/Zotero/storage/G5IEYMP7/Fonov et al. - 2011 - Unbiased Average Age-Appropriate Atlases for Pedia.pdf:application/pdf;PubMed Central Full Text PDF:/Users/adebimpe/Zotero/storage/VERJ7IH6/Fonov et al. - 2011 - Unbiased Average Age-Appropriate Atlases for Pedia.pdf:application/pdf},
}

@article{power_fd_dvars,
	title = {Methods to detect, characterize, and remove motion artifact in resting state {fMRI}},
	volume = {84},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.08.048},
	abstract = {Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.},
	language = {eng},
	journal = {NeuroImage},
	author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
	month = jan,
	year = {2014},
	pmid = {23994314},
	pmcid = {PMC3849338},
	keywords = {Adolescent, Adult, Algorithms, Artifact, Artifacts, Brain, Brain Mapping, Female, Functional connectivity, Head Movements, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Movement, MRI, Pattern Recognition, Automated, Reproducibility of Results, Rest, Resting state, Sensitivity and Specificity, Subtraction Technique, Young Adult},
	pages = {320--341},
	file = {Accepted Version:/Users/adebimpe/Zotero/storage/2GWXNAAS/Power et al. - 2014 - Methods to detect, characterize, and remove motion.pdf:application/pdf;Accepted Version:/Users/adebimpe/Zotero/storage/XXF8TDNA/Power et al. - 2014 - Methods to detect, characterize, and remove motion.pdf:application/pdf},
}

@article{nilearn,
	title = {Machine learning for neuroimaging with scikit-learn},
	volume = {0},
	issn = {1662-5196},
	url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
	doi = {10.3389/fninf.2014.00014},
	abstract = {Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.},
	language = {English},
	urldate = {2021-07-23},
	journal = {Frontiers in Neuroinformatics},
	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},
	year = {2014},
	note = {Publisher: Frontiers},
	keywords = {machine learning, Neuroimaging, python, scikit-learn, statistical learning},
	file = {Full Text:/Users/adebimpe/Zotero/storage/YCCCJVSR/Abraham et al. - 2014 - Machine learning for neuroimaging with scikit-lear.pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/UB8HDBUH/Abraham et al. - 2014 - Machine learning for neuroimaging with scikit-lear.pdf:application/pdf},
}

@article{hcppipelines,
	title = {The minimal preprocessing pipelines for the {Human} {Connectome} {Project}},
	volume = {80},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2013.04.127},
	abstract = {The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.},
	language = {eng},
	journal = {NeuroImage},
	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 and {WU-Minn HCP Consortium}},
	month = oct,
	year = {2013},
	pmid = {23668970},
	pmcid = {PMC3720813},
	keywords = {Algorithms, Brain, CIFTI, Connectome, Diffusion Tensor Imaging, Grayordinates, Human Connectome Project, Humans, Image analysis pipeline, Image Interpretation, Computer-Assisted, Models, Anatomic, Models, Neurological, Multi-modal data integration, Nerve Net, Surface-based analysis},
	pages = {105--124},
	file = {Full Text:/Users/adebimpe/Zotero/storage/VKFJ6Q5Q/Glasser et al. - 2013 - The minimal preprocessing pipelines for the Human .pdf:application/pdf;Full Text:/Users/adebimpe/Zotero/storage/U43PJW87/Glasser et al. - 2013 - The minimal preprocessing pipelines for the Human .pdf:application/pdf},
}

@article{mindboggle,
	title = {Mindboggling morphometry of human brains},
	volume = {13},
	issn = {1553-7358},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
	doi = {10.1371/journal.pcbi.1005350},
	abstract = {Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.},
	language = {en},
	number = {2},
	urldate = {2021-07-28},
	journal = {PLOS Computational Biology},
	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},
	month = feb,
	year = {2017},
	note = {Publisher: Public Library of Science},
	keywords = {Algorithms, Biomarkers, Central nervous system, Computer software, Geodesics, Magnetic resonance imaging, Neuroimaging, Open source software},
	pages = {e1005350},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/K8LWVBER/Klein et al. - 2017 - Mindboggling morphometry of human brains.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/676Y56K4/article.html:text/html},
}

@article{mni152lin,
	title = {A probabilistic atlas of the human brain: theory and rationale for its development. {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
	volume = {2},
	issn = {1053-8119},
	shorttitle = {A probabilistic atlas of the human brain},
	doi = {10.1006/nimg.1995.1012},
	language = {eng},
	number = {2},
	journal = {NeuroImage},
	author = {Mazziotta, J. C. and Toga, A. W. and Evans, A. and Fox, P. and Lancaster, J.},
	month = jun,
	year = {1995},
	pmid = {9343592},
	keywords = {Brain Mapping, Humans, Image Processing, Computer-Assisted, International Cooperation, Magnetic Resonance Imaging, Models, Statistical, Reference Values, Software, Stereotaxic Techniques, Terminology as Topic},
	pages = {89--101},
}

@article{mni152nlin6asym,
	title = {Brain templates and atlases},
	volume = {62},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2012.01.024},
	abstract = {The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution.},
	language = {eng},
	number = {2},
	journal = {NeuroImage},
	author = {Evans, Alan C. and Janke, Andrew L. and Collins, D. Louis and Baillet, Sylvain},
	month = aug,
	year = {2012},
	pmid = {22248580},
	keywords = {Anatomy, Artistic, Atlases as Topic, Brain, Brain Mapping, History, 20th Century, History, 21st Century, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Neuroimaging},
	pages = {911--922},
}

@article{fieldmapless1,
	title = {Evaluation of {Field} {Map} and {Nonlinear} {Registration} {Methods} for {Correction} of {Susceptibility} {Artifacts} in {Diffusion} {MRI}},
	volume = {11},
	issn = {1662-5196},
	doi = {10.3389/fninf.2017.00017},
	abstract = {Correction of echo planar imaging (EPI)-induced distortions (called "unwarping") improves anatomical fidelity for diffusion magnetic resonance imaging (MRI) and functional imaging investigations. Commonly used unwarping methods require the acquisition of supplementary images during the scanning session. Alternatively, distortions can be corrected by nonlinear registration to a non-EPI acquired structural image. In this study, we compared reliability using two methods of unwarping: (1) nonlinear registration to a structural image using symmetric normalization (SyN) implemented in Advanced Normalization Tools (ANTs); and (2) unwarping using an acquired field map. We performed this comparison in two different test-retest data sets acquired at differing sites (N = 39 and N = 32). In both data sets, nonlinear registration provided higher test-retest reliability of the output fractional anisotropy (FA) maps than field map-based unwarping, even when accounting for the effect of interpolation on the smoothness of the images. In general, field map-based unwarping was preferable if and only if the field maps were acquired optimally.},
	language = {eng},
	journal = {Frontiers in Neuroinformatics},
	author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
	year = {2017},
	pmid = {28270762},
	pmcid = {PMC5318394},
	keywords = {B0 field mapping, diffusion tensor imaging (DTI), EPI distortion correction, reliability, symmetric normalization registration},
	pages = {17},
	file = {Full Text:/Users/adebimpe/Zotero/storage/LMZVXAKL/Wang et al. - 2017 - Evaluation of Field Map and Nonlinear Registration.pdf:application/pdf},
}

@article{fieldmapless2,
	title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and {T1} images},
	doi = {10.7490/F1000RESEARCH.1096036.1},
	abstract = {[1] Hutton et al., 2002. Image Distortion Correction in fMRI: A Quantitative Evaluation. NeuroImage, 16:217–240. [2] Jezzard \& Balaban, 1995. Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med 34:65-73. [3] Andersson et al., 2003. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 20:870–88. [4] Kybic et al., 2000. Unwarping of unidirectionally distorted EPI images. IEEE Trans Med Imag , 19:80–93. [5] Studholme et al., 2000. Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model. IEEE Trans Med Imag , 19:1115–27. [6] Gholipour et al., 2008. Cross-Validation of Deformable Registration With Field Maps, IEEE J Sel Top Sign. , 2:854–869. [7] Avants et al. , 2008. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Imag Anal, 12:26–41. [8] Gorgolewski et al., 2011. Nipype: a flexible , lightweight and extensible neuroimaging data processing framework in Python. Front Neuroinform, 5:13. [9] Kahnt et al., 2012. Connectivity-based parcellation of the human orbitofrontal cortex. J Neurosci 32:6240–50. References For each method, coregistered mean EPI images of all subjects were projected into MNI space, concatenated and averaged over the 4th dimension. The four detail images depict the white box outlined in the whole brain image for each method (x=-8mm). White matter and mask edges of the MNI152 standard brain are overlayed for anatomical reference. While nonlinear coregistration achieves a better fit of the brain outline in frontal regions (white asterisks), fieldmap and topup approach show superior performance in matching gray-white matter boundaries (black asterisk). Different methods for distortion correction show characteristic coregistration outcomes 1},
	author = {Huntenburg, Julia M. and Gorgolewski, Krzysztof J. and Anwander, A. and Margulies, D.},
	year = {2014},
}

@article{fieldmapless3,
	title = {Characterization and {Correction} of {Geometric} {Distortions} in 814 {Diffusion} {Weighted} {Images}},
	volume = {11},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
	doi = {10.1371/journal.pone.0152472},
	abstract = {Introduction Diffusion Weighted Imaging (DWI), which is based on Echo Planar Imaging (EPI) protocols, is becoming increasingly important for neurosurgical applications. However, its use in this context is limited in part by significant spatial distortion inherent to EPI. Method We evaluated an efficient algorithm for EPI distortion correction (EPIC) across 814 DWI scans from 250 brain tumor patients and quantified the magnitude of geometric distortion for whole brain and multiple brain regions. Results Evaluation of the algorithm’s performance revealed significantly higher mutual information between T1-weighted pre-contrast images and corrected b = 0 images than the uncorrected b = 0 images (p {\textless} 0.001). The distortion magnitude across all voxels revealed a median EPI distortion effect of 2.1 mm, ranging from 1.2 mm to 5.9 mm, the 5th and 95th percentile, respectively. Regions adjacent to bone-air interfaces, such as the orbitofrontal cortex, temporal poles, and brain stem, were the regions most severely affected by DWI distortion. Conclusion Using EPIC to estimate the degree of distortion in 814 DWI brain tumor images enabled the creation of a topographic atlas of DWI distortion across the brain. The degree of displacement of tumors boundaries in uncorrected images is severe but can be corrected for using EPIC. Our results support the use of distortion correction to ensure accurate and careful application of DWI to neurosurgical practice.},
	language = {en},
	number = {3},
	urldate = {2021-07-28},
	journal = {PLOS ONE},
	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.},
	month = mar,
	year = {2016},
	note = {Publisher: Public Library of Science},
	keywords = {Algorithms, Brainstem, Cancers and neoplasms, Diffusion weighted imaging, Fluid dynamics, Imaging techniques, Magnetic resonance imaging, Neuroimaging},
	pages = {e0152472},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/2LQFY7NV/Treiber et al. - 2016 - Characterization and Correction of Geometric Disto.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/UY6LIHBG/article.html:text/html},
}

@article{lanczos,
	title = {Evaluation of {Noisy} {Data}},
	volume = {1},
	issn = {0887-459X},
	url = {https://epubs.siam.org/doi/abs/10.1137/0701007},
	doi = {10.1137/0701007},
	number = {1},
	urldate = {2021-07-28},
	journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
	author = {Lanczos, C.},
	month = jan,
	year = {1964},
	note = {Publisher: Society for Industrial and Applied Mathematics},
	pages = {76--85},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/HCQ5J6PF/Lanczos - 1964 - Evaluation of Noisy Data.pdf:application/pdf},
}

@article{posse_t2s,
	title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
	volume = {42},
	issn = {0740-3194},
	doi = {10.1002/(sici)1522-2594(199907)42:1<87::aid-mrm13>3.0.co;2-o},
	abstract = {Improved data acquisition and processing strategies for blood oxygenation level-dependent (BOLD)-contrast functional magnetic resonance imaging (fMRI), which enhance the functional contrast-to-noise ratio (CNR) by sampling multiple echo times in a single shot, are described. The dependence of the CNR on T2*, the image encoding time, and the number of sampled echo times are investigated for exponential fitting, echo summation, weighted echo summation, and averaging of correlation maps obtained at different echo times. The method is validated in vivo using visual stimulation and turbo proton echoplanar spectroscopic imaging (turbo-PEPSI), a new single-shot multi-slice MR spectroscopic imaging technique, which acquires up to 12 consecutive echoplanar images with echo times ranging from 12 to 213 msec. Quantitative T2*-mapping significantly increases the measured extent of activation and the mean correlation coefficient compared with conventional echoplanar imaging. The sensitivity gain with echo summation, which is computationally efficient provides similar sensitivity as fitting. For all data processing methods sensitivity is optimum when echo times up to 3.2 T2* are sampled. This methodology has implications for comparing functional sensitivity at different magnetic field strengths and between brain regions with different magnetic field inhomogeneities.},
	language = {eng},
	number = {1},
	journal = {Magnetic Resonance in Medicine},
	author = {Posse, S. and Wiese, S. and Gembris, D. and Mathiak, K. and Kessler, C. and Grosse-Ruyken, M. L. and Elghahwagi, B. and Richards, T. and Dager, S. R. and Kiselev, V. G.},
	month = jul,
	year = {1999},
	pmid = {10398954},
	keywords = {Adult, Brain, Computer Simulation, Echo-Planar Imaging, Humans, Image Enhancement, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Oxygen, Reference Values, Sensitivity and Specificity, Visual Perception},
	pages = {87--97},
	file = {Full Text:/Users/adebimpe/Zotero/storage/6GR5B2F4/Posse et al. - 1999 - Enhancement of BOLD-contrast sensitivity by single.pdf:application/pdf},
}

@article{cbfqc,
    author = { Dolui, Sudipto and Wolf, Ronald  and Nabavizadeh, Seyed Ali  and Wolk, David A.  and Detre, John A.},
    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{scrub_dolui,
    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 = {Dolui, Sudipto and Wolk, David A. and Wolk, David A. and Detre, John A. },
    year = {2016}
}



@article{chappell_basil,
	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,
	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,
	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}
}


@article{numpy,
	title = {Array programming with {NumPy}},
	volume = {585},
	copyright = {2020 The Author(s)},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/s41586-020-2649-2},
	doi = {10.1038/s41586-020-2649-2},
	abstract = {Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.},
	language = {en},
	number = {7825},
	urldate = {2021-07-23},
	journal = {Nature},
	author = {Harris, Charles R. and Millman, Jarrod K.  and van der Walt, Stéfan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H. and Brett, Matthew and Haldane, Allan and del Río, Jaime Fernández and Wiebe, Mark and Peterson, Pearu and Gérard-Marchant, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
	month = sep,
	year = {2020},
	note = {Bandiera\_abtest: a computer-science;software;solar-physics},
	pages = {357--362},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/ZLJBSN8B/Harris et al. - 2020 - Array programming with NumPy.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/2S9QHZBQ/s41586-020-2649-2.html:text/html},
}

@article{scipy,
	title = {{SciPy} 1.0: fundamental algorithms for scientific computing in {Python}},
	volume = {17},
	copyright = {2020 The Author(s)},
	issn = {1548-7105},
	shorttitle = {{SciPy} 1.0},
	url = {https://www.nature.com/articles/s41592-019-0686-2},
	doi = {10.1038/s41592-019-0686-2},
	abstract = {SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.},
	language = {en},
	number = {3},
	urldate = {2021-07-23},
	journal = {Nature Methods},
	author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and van der Walt, Stéfan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C. J. and Polat, İlhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Antônio H. and Pedregosa, Fabian and van Mulbregt, Paul},
	month = mar,
	year = {2020},
	note = {Bandiera\_abtest:},
	pages = {261--272},
	file = {Full Text PDF:/Users/adebimpe/Zotero/storage/HBFK9K6T/Virtanen et al. - 2020 - SciPy 1.0 fundamental algorithms for scientific c.pdf:application/pdf;Snapshot:/Users/adebimpe/Zotero/storage/F87IPJ7C/s41592-019-0686-2.html:text/html},
}