Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.
flirt
with boundary-based registration (BBR) metric - 6 dof/usr/local/miniconda/bin/aslprep /bidsdir /out participant -w /wk_dir --skip_bids_validation --output-spaces func anat MNI152NLin2009cAsym --participant-label 01 --fs-license-file /opt/freesurfer/license.txt --force-bbr --fs-no-reconall
We kindly ask to report results preprocessed with this tool using the following boilerplate.
Results included in this manuscript come from preprocessing performed using aslprep 0.2.3, which is based on Nipype 1.5.0 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502).
A total of 1 T1-weighted (T1w) images were found within the input BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection
(Tustison et al. 2010), distributed with ANTs 2.3.1 (Avants et al. 2008, RRID:SCR_004757), and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype implementation of the antsBrainExtraction.sh
workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast
(FSL 6.0.3:b862cdd5, RRID:SCR_002823, Zhang, Brady, and Smith 2001). Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with antsRegistration
(ANTs 2.3.1), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: ICBM 152 Nonlinear Asymmetrical template version 2009c [Fonov et al. (2009), RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],
For each of the 1 ASL runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt
(FSL 6.0.3:b862cdd5, Jenkinson et al. 2002). The BOLD reference was then co-registered to the T1w reference using flirt
(FSL 6.0.3:b862cdd5, Jenkinson and Smith 2001) with the boundary-based registration (Greve and Fischl 2009) cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The CBF was quantified from preproccessed ASL data using a relatively basic model (Detre et al. 1992) (Alsop et al. 2015). CBF are susceptible to artifacts due to low signal to noise ratio and sensitivity to motion, Structural Correlation based Outlier Rejection (SCORE) algothim was applied to the CBF to discard few extreme outliers (Dolui et al. 2017). Furthermore,Structural Correlation with RobUst Bayesian (SCRUB) algorithms was applied to the CBF by iteratively reweighted CBF with structural tissues probalility maps (Sudipto Dolui David A. Wolk and Detre 2016). Alternate method of CBF computation is Bayesian Inference for Arterial Spin Labeling (BASIL) as implmented in FSL which is based on Bayeisan inference principles (Chappell et al. 2009). BASIL computed the CBF from ASL incoporating natural varaibility of other model parameters and spatial regularization of the estimated perfusion image. BASIL also included correction for partial volume effects (Chappell et al. 2011). The ASL and CBF dreivatives were resampled into standard space, generating a preprocessed ASL and computed CBF in MNI152NLin2009cAsym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Several confounding time-series were calculated based on the preprocessed ASL: framewise displacement (FD) and DVARS. FD and DVARS are calculated for each ASL run, both using their implementations in Nipype (following the definitions by Power et al. 2014). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file.
The following quality control (qc) measures was estimated: framewise displacement and relative root mean square dice index. Other qc meaure include dice and jaccard indices, cross-correlation and coverage that estimate the coregistration quality of ASL and T1W images and normalization quality of ASL to template. Quality evaluation index (QEI) was also computed for CBF (S. A. N. Sudipto Dolui Ronald Wolf 2016). The QEI is automated for objective quality evaluation of CBF maps and measured the CBF quality based on structural similarity,spatial variability and the percentatge of voxels with negtaive CBF within Grey matter All resamplings can be performed with a single interpolation step by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using antsApplyTransforms
(ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels (Lanczos 1964). Non-gridded (surface) resamplings were performed using mri_vol2surf
(FreeSurfer).
Many internal operations of aslprep use Nilearn 0.6.2 (Abraham et al. 2014, RRID:SCR_001362), mostly within the functional processing workflow. For more details of the pipeline, see the section corresponding to workflows in aslprep’s documentation.
The above boilerplate text was automatically generated by aslprep with the express intention that users should copy and paste this text into their manuscripts unchanged. It is released under the CC0 license.
Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” Frontiers in Neuroinformatics 8. https://doi.org/10.3389/fninf.2014.00014.
Alsop, David C., John A. Detre, Xavier Golay, Matthias Günther, Jeroen Hendrikse, Luis Hernandez-Garcia, Hanzhang Lu, et al. 2015. “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.” Magnetic Resonance in Medicine, no. 1. https://doi.org/10.1002/mrm.25197.
Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” Medical Image Analysis 12 (1): 26–41. https://doi.org/10.1016/j.media.2007.06.004.
Chappell, M. A., A. R. Groves, B. J. MacIntosh, M. J. Donahue, P. Jezzard, and M. W. Woolrich. 2011. “Partial Volume Correction of Multiple Inversion Time Arterial Spin Labeling MRI Data.” Magnetic Resonance in Medicine 65 (4). https://doi.org/10.1002/mrm.22641.
Chappell, Michael A., Adrian R. Groves, Brandon Whitcher, and Mark W. Woolrich. 2009. “Variational Bayesian Inference for a Nonlinear Forward Model.” IEEE Transactions on Signal Processing 57 (1). https://doi.org/10.1109/TSP.2008.2005752.
Detre, John A., John S. Leigh, Donald S. Williams, and Alan P. Koretsky. 1992. “Perfusion Imaging.” Magnetic Resonance in Medicine 23 (1). https://doi.org/10.1002/mrm.1910230106.
Dolui, Sudipto, Ze Wang, Russell T. Shinohara, David A. Wolk, John A. Detre, and for the Alzheimer’s Disease Neuroimaging Initiative. 2017. “Structural Correlation-Based Outlier Rejection (SCORE) Algorithm for Arterial Spin Labeling Time Series: SCORE: Denoising Algorithm for ASL.” Journal of Magnetic Resonance Imaging 45 (6). https://doi.org/10.1002/jmri.25436.
Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” NeuroImage 47, Supplement 1: S102. https://doi.org/10.1016/S1053-8119(09)70884-5.
Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” Frontiers in Neuroinformatics 5: 13. https://doi.org/10.3389/fninf.2011.00013.
Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” Software. Zenodo. https://doi.org/10.5281/zenodo.596855.
Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” NeuroImage 48 (1): 63–72. https://doi.org/10.1016/j.neuroimage.2009.06.060.
Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” NeuroImage 17 (2): 825–41. https://doi.org/10.1006/nimg.2002.1132.
Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” Medical Image Analysis 5 (2): 143–56. https://doi.org/10.1016/S1361-8415(01)00036-6.
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Sudipto Dolui, David A. Wolk, and John A. Detre. 2016. “SCRUB: A Structural Correlation and Empirical Robust Bayesian Method for Asl Data.” International Society for Magnetic Resonance in Medicine, no. 1. https://doi.org/http://archive.ismrm.org/2016/2880.html.
Sudipto Dolui, Seyed Ali Nabavizadeh, Ronald Wolf. 2016. “Automated Quality Evaluation Index for 2D Asl Cbf Maps.” International Society for Magnetic Resonance in Medicine, no. 1. https://doi.org/http://indexsmart.mirasmart.com/ISMRM2017/PDFfiles/0682.html.
Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” IEEE Transactions on Medical Imaging 29 (6): 1310–20. https://doi.org/10.1109/TMI.2010.2046908.
Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” IEEE Transactions on Medical Imaging 20 (1): 45–57. https://doi.org/10.1109/42.906424.
Results included in this manuscript come from preprocessing performed using *aslprep* 0.2.3, which is based on *Nipype* 1.5.0 (@nipype1; @nipype2; RRID:SCR_002502). Anatomical data preprocessing : A total of 1 T1-weighted (T1w) images were found within the input BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.3.1 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a *Nipype* implementation of the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using `fast` [FSL 6.0.3:b862cdd5, RRID:SCR_002823, @fsl_fast]. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with `antsRegistration` (ANTs 2.3.1), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], Functional data preprocessing : For each of the 1 ASL runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of *fMRIPrep*. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using `mcflirt` [FSL 6.0.3:b862cdd5, @mcflirt]. The BOLD reference was then co-registered to the T1w reference using `flirt` [FSL 6.0.3:b862cdd5, @flirt] with the boundary-based registration [@bbr] cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as *preprocessed BOLD in original space*, or just *preprocessed BOLD*. The CBF was quantified from *preproccessed* ASL data using a relatively basic model [@detre_perfusion_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}
@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. 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