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<body>
<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
<div class="collapse navbar-collapse">
<ul class="navbar-nav">
<li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
<li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
<li class="nav-item"><a class="nav-link" href="#About">About</a></li>
<li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
<li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
</ul>
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<noscript>
<h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
</noscript>
<div id="Summary">
<h1 class="sub-report-title">Summary</h1>
<div id="datatype-figures_desc-summary_suffix-T1w">
<ul class="elem-desc">
<li>Subject ID: 01</li>
<li>Structural images: 2 T1-weighted (+ 2 T2-weighted)</li>
<li>Functional series: 0</li>
<li>Standard output spaces: MNI152NLin2009cAsym, MNI152NLin6Asym, fsaverage</li>
<li>Non-standard output spaces: </li>
<li>FreeSurfer reconstruction: Run by fMRIPrep</li>
</ul>
</div>
</div>
<div id="Anatomical">
<h1 class="sub-report-title">Anatomical</h1>
<div id="datatype-figures_desc-conform_extension-['.html']_suffix-T1w">
<h3 class="elem-title">Anatomical Conformation</h3>
<ul class="elem-desc">
<li>Input T1w images: 2</li>
<li>Output orientation: RAS</li>
<li>Output dimensions: 208x300x320</li>
<li>Output voxel size: 0.8mm x 0.8mm x 0.8mm</li>
<li>Discarded images: 0</li>
</ul>
</div>
<div id="datatype-figures_suffix-dseg">
<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p> <img class="svg-reportlet" src="./sub-01/figures/sub-01_dseg.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-01/figures/sub-01_dseg.svg" target="_blank">sub-01/figures/sub-01_dseg.svg</a>
</div>
</div>
<div id="datatype-figures_regex_search-True_space-.*_suffix-T1w">
<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">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.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin6Asym</code> template.</p> <object class="svg-reportlet" type="image/svg+xml" data="./sub-01/figures/sub-01_space-MNI152NLin6Asym_T1w.svg">
Problem loading figure sub-01/figures/sub-01_space-MNI152NLin6Asym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-01/figures/sub-01_space-MNI152NLin6Asym_T1w.svg" target="_blank">sub-01/figures/sub-01_space-MNI152NLin6Asym_T1w.svg</a>
</div>
<p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p> <object class="svg-reportlet" type="image/svg+xml" data="./sub-01/figures/sub-01_space-MNI152NLin2009cAsym_T1w.svg">
Problem loading figure sub-01/figures/sub-01_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
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Get figure file: <a href="./sub-01/figures/sub-01_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-01/figures/sub-01_space-MNI152NLin2009cAsym_T1w.svg</a>
</div>
</div>
<div id="datatype-figures_desc-reconall_suffix-T1w">
<h3 class="run-title">Surface reconstruction</h3><p class="elem-caption">Surfaces (white and pial) reconstructed with FreeSurfer (<code>recon-all</code>) overlaid on the participant's T1w template.</p> <img class="svg-reportlet" src="./sub-01/figures/sub-01_desc-reconall_T1w.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-01/figures/sub-01_desc-reconall_T1w.svg" target="_blank">sub-01/figures/sub-01_desc-reconall_T1w.svg</a>
</div>
</div>
</div>
<div id="About">
<h1 class="sub-report-title">About</h1>
<div id="datatype-figures_desc-about_suffix-T1w">
<ul>
<li>fMRIPrep version: 20.2.1</li>
<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep -w /work --participant-label 01 --anat-only --bids-database-dir /lustre03/project/6003287/datasets/cneuromod_processed/smriprep/sourcedata/raw/.pybids_cache --bids-filter-file /lustre03/project/6003287/datasets/cneuromod_processed/smriprep/sourcedata/raw/code/bids_filters.json --output-layout bids --output-spaces MNI152NLin2009cAsym MNI152NLin6Asym --cifti-output 91k --skip_bids_validation --write-graph --omp-nthreads 8 --nprocs 16 --mem_mb 65536 /lustre03/project/6003287/datasets/cneuromod_processed/smriprep/sourcedata/raw /lustre03/project/6003287/datasets/cneuromod_processed/smriprep participant</code></li>
<li>Date preprocessed: 2021-05-22 01:25:42 +0000</li>
</ul>
</div>
</div>
</div>
<div id="boilerplate">
<h1 class="sub-report-title">Methods</h1>
<p>We kindly ask to report results preprocessed with this tool using the following
boilerplate.</p>
<ul class="nav nav-tabs" id="myTab" role="tablist">
<li class="nav-item">
<a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
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<li class="nav-item">
<a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
</li>
<li class="nav-item">
<a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
</li>
</ul>
<div class="tab-content" id="myTabContent">
<div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.2.1 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.5.1 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
<dl>
<dt>Anatomical data preprocessing</dt>
<dd><p>A total of 2 T1-weighted (T1w) images were found within the input BIDS dataset. All of them were corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.3.3 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> 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 <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. A T1w-reference map was computed after registration of 2 T1w images (after INU-correction) using <code>mri_robust_template</code> <span class="citation" data-cites="fs_template">(FreeSurfer 6.0.1, Reuter, Rosas, and Fischl 2010)</span>. Brain surfaces were reconstructed using <code>recon-all</code> <span class="citation" data-cites="fs_reconall">(FreeSurfer 6.0.1, RRID:SCR_001847, Dale, Fischl, and Sereno 1999)</span>, and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle <span class="citation" data-cites="mindboggle">(RRID:SCR_002438, Klein et al. 2017)</span>. Volume-based spatial normalization to two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.3.3), using brain-extracted versions of both T1w reference and the T1w template. The following templates were selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], <em>FSL’s MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model</em> [<span class="citation" data-cites="mni152nlin6asym">Evans et al. (2012)</span>, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym],</p>
</dd>
</dl>
<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep's documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
<h3 id="copyright-waiver">Copyright Waiver</h3>
<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
<h3 id="references" class="unnumbered">References</h3>
<div id="refs" class="references">
<div id="ref-nilearn">
<p>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.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
</div>
<div id="ref-ants">
<p>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.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
</div>
<div id="ref-fs_reconall">
<p>Dale, Anders M., Bruce Fischl, and Martin I. Sereno. 1999. “Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction.” <em>NeuroImage</em> 9 (2): 179–94. <a href="https://doi.org/10.1006/nimg.1998.0395" class="uri">https://doi.org/10.1006/nimg.1998.0395</a>.</p>
</div>
<div id="ref-fmriprep2">
<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
</div>
<div id="ref-fmriprep1">
<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
</div>
<div id="ref-mni152nlin6asym">
<p>Evans, AC, AL Janke, DL Collins, and S Baillet. 2012. “Brain Templates and Atlases.” <em>NeuroImage</em> 62 (2): 911–22. <a href="https://doi.org/10.1016/j.neuroimage.2012.01.024" class="uri">https://doi.org/10.1016/j.neuroimage.2012.01.024</a>.</p>
</div>
<div id="ref-mni152nlin2009casym">
<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
</div>
<div id="ref-nipype1">
<p>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.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
</div>
<div id="ref-nipype2">
<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
</div>
<div id="ref-mindboggle">
<p>Klein, Arno, Satrajit S. Ghosh, Forrest S. Bao, Joachim Giard, Yrjö Häme, Eliezer Stavsky, Noah Lee, et al. 2017. “Mindboggling Morphometry of Human Brains.” <em>PLOS Computational Biology</em> 13 (2): e1005350. <a href="https://doi.org/10.1371/journal.pcbi.1005350" class="uri">https://doi.org/10.1371/journal.pcbi.1005350</a>.</p>
</div>
<div id="ref-fs_template">
<p>Reuter, Martin, Herminia Diana Rosas, and Bruce Fischl. 2010. “Highly Accurate Inverse Consistent Registration: A Robust Approach.” <em>NeuroImage</em> 53 (4): 1181–96. <a href="https://doi.org/10.1016/j.neuroimage.2010.07.020" class="uri">https://doi.org/10.1016/j.neuroimage.2010.07.020</a>.</p>
</div>
<div id="ref-n4">
<p>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.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
</div>
<div id="ref-fsl_fast">
<p>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.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
</div>
</div></div></div>
<div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
Results included in this manuscript come from preprocessing
performed using *fMRIPrep* 20.2.1
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.5.1
(@nipype1; @nipype2; RRID:SCR_002502).
Anatomical data preprocessing
: A total of 2 T1-weighted (T1w) images were found within the input
BIDS dataset.
All of them were corrected for intensity non-uniformity (INU)
with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.3.3 [@ants, RRID:SCR_004757].
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 5.0.9, RRID:SCR_002823,
@fsl_fast].
A T1w-reference map was computed after registration of
2 T1w images (after INU-correction) using
`mri_robust_template` [FreeSurfer 6.0.1, @fs_template].
Brain surfaces were reconstructed using `recon-all` [FreeSurfer 6.0.1,
RRID:SCR_001847, @fs_reconall], and the brain mask estimated
previously was refined with a custom variation of the method to reconcile
ANTs-derived and FreeSurfer-derived segmentations of the cortical
gray-matter of Mindboggle [RRID:SCR_002438, @mindboggle].
Volume-based spatial normalization to two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was performed through
nonlinear registration with `antsRegistration` (ANTs 2.3.3),
using brain-extracted versions of both T1w reference and the T1w template.
The following templates were selected for spatial normalization:
*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], *FSL's MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model* [@mni152nlin6asym, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym],
Many internal operations of *fMRIPrep* 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 *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
### Copyright Waiver
The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
</pre>
</div>
<div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
using \emph{fMRIPrep} 20.2.1 (\citet{fmriprep1}; \citet{fmriprep2};
RRID:SCR\_016216), which is based on \emph{Nipype} 1.5.1
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
\begin{description}
\item[Anatomical data preprocessing]
A total of 2 T1-weighted (T1w) images were found within the input BIDS
dataset. All of them were corrected for intensity non-uniformity (INU)
with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with ANTs
2.3.3 \citep[RRID:SCR\_004757]{ants}. 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 5.0.9,
RRID:SCR\_002823,][]{fsl_fast}. A T1w-reference map was computed after
registration of 2 T1w images (after INU-correction) using
\texttt{mri\_robust\_template} \citep[FreeSurfer 6.0.1,][]{fs_template}.
Brain surfaces were reconstructed using \texttt{recon-all}
\citep[FreeSurfer 6.0.1, RRID:SCR\_001847,][]{fs_reconall}, and the
brain mask estimated previously was refined with a custom variation of
the method to reconcile ANTs-derived and FreeSurfer-derived
segmentations of the cortical gray-matter of Mindboggle
\citep[RRID:SCR\_002438,][]{mindboggle}. Volume-based spatial
normalization to two standard spaces (MNI152NLin2009cAsym,
MNI152NLin6Asym) was performed through nonlinear registration with
\texttt{antsRegistration} (ANTs 2.3.3), using brain-extracted versions
of both T1w reference and the T1w template. The following templates were
selected for spatial normalization: \emph{ICBM 152 Nonlinear
Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]}, \emph{FSL's
MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain
Stereotaxic Registration Model} {[}\citet{mni152nlin6asym},
RRID:SCR\_002823; TemplateFlow ID: MNI152NLin6Asym{]},
\end{description}
Many internal operations of \emph{fMRIPrep} 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://fmriprep.readthedocs.io/en/latest/workflows.html}{the
section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
\hypertarget{copyright-waiver}{%
\subsubsection{Copyright Waiver}\label{copyright-waiver}}
The above boilerplate text was automatically generated by fMRIPrep with
the express intention that users should copy and paste this text into
their manuscripts \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/fmriprep/data/boilerplate.bib}</pre>
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</pre>
</div>
</div>
</div>
<div id="errors">
<h1 class="sub-report-title">Errors</h1>
<p>No errors to report!</p>
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