nipreps-data
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bootcamp-geneva-2024


			
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    <div id="Summary" class="mt-5">
    <h1 class="sub-report-title pt-5 ps-4">Summary</h1>
        <div id="datatype-figures_desc-summary_subject-15_suffix-T1w" class="ps-4 pe-4 mb-2">
                    <ul class="elem-desc">
		<li>Subject ID: 15</li>
		<li>Structural images: 1 T1-weighted (+ 1 T2-weighted)</li>
		<li>Functional series: 2</li>
		<ul class="elem-desc">
			<li>Task: mixed (1 run)</li>
			<li>Task: rest (1 run)</li>
		</ul>
		<li>Standard output spaces: MNI152NLin2009cAsym</li>
		<li>Non-standard output spaces: </li>
		<li>FreeSurfer reconstruction: Run by fMRIPrep</li>
	</ul>
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    <div id="Anatomical" class="mt-5">
    <h1 class="sub-report-title pt-5 ps-4">Anatomical</h1>
        <div id="datatype-figures_desc-conform_extension-['.html']_subject-15_suffix-T1w" class="ps-4 pe-4 mb-2">
                    <h3 class="elem-title">Anatomical Conformation</h3>
		<ul class="elem-desc">
			<li>Input T1w images: 1</li>
			<li>Output orientation: RAS</li>
			<li>Output dimensions: 224x272x288</li>
			<li>Output voxel size: 0.8mm x 0.8mm x 0.8mm</li>
			<li>Discarded images: 0</li>

		</ul>
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        <div id="datatype-figures_subject-15_suffix-dseg" class="ps-4 pe-4 mb-2">
<h3 class="run-title mt-3">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the final, preprocessed T1-weighted image, with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <div class="reportlet">
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<small>Get figure file: <a href="./sub-15/figures/sub-15_acq-denoised_dseg.svg" target="_blank">sub-15/figures/sub-15_acq-denoised_dseg.svg</a></small>

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        <div id="datatype-figures_regex_search-True_space-.*_subject-15_suffix-T1w" class="ps-4 pe-4 mb-2">
<h3 class="run-title mt-3">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>MNI152NLin2009cAsym</code> template.</p>                    <div class="reportlet">
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<small>Get figure file: <a href="./sub-15/figures/sub-15_acq-denoised_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-15/figures/sub-15_acq-denoised_space-MNI152NLin2009cAsym_T1w.svg</a></small>

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<h3 class="run-title mt-3">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>                    <div class="reportlet">
<img class="svg-reportlet" src="./sub-15/figures/sub-15_acq-denoised_desc-reconall_T1w.svg" style="width: 100%" />
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    <div id="About" class="mt-5">
    <h1 class="sub-report-title pt-5 ps-4">About</h1>
        <div id="datatype-figures_desc-about_subject-15_suffix-T1w" class="ps-4 pe-4 mb-2">
                    <ul>
		<li>fMRIPrep version: 24.1.0.dev28+gfe7c9ff8.d20240822</li>
		<li>fMRIPrep command: <code>/home/oesteban/.miniconda3/envs/fmriprep/bin/fmriprep /data/datasets/bootcamp/bidsdata /data/derivatives/bootcamp-geneva/fmriprep-24.0.0/ participant --nprocs 8 --omp-nthreads 12 -w work -vv --skip-bids-validation --anat-only --bids-filter-file bids-filters.json</code></li>
		<li>Date preprocessed: 2024-09-02 14:22:58 +0200</li>
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            <div class="boiler-html p-3 m-4 bg-primary" style="--bs-bg-opacity: .04; font-family: 'Bitstream Charter', 'Georgia', Times;"><p>Results included in this manuscript come from preprocessing performed
using <em>fMRIPrep</em> 24.1.0.dev28+gfe7c9ff8.d20240822 (<span
class="citation" data-cites="fmriprep1">Esteban et al. (2019)</span>;
<span class="citation" data-cites="fmriprep2">Esteban et al.
(2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.8.6
(<span class="citation" data-cites="nipype1">K. Gorgolewski et al.
(2011)</span>; <span class="citation" data-cites="nipype2">K. J.
Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
<dl>
<dt>Anatomical data preprocessing</dt>
<dd>
<p>A total of 1 T1-weighted (T1w) images were found within the input
BIDS dataset. The T1w image was corrected for intensity non-uniformity
(INU) with <code>N4BiasFieldCorrection</code> <span class="citation"
data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs
2.5.1 <span class="citation" data-cites="ants">(Avants et al. 2008,
RRID:SCR_004757)</span>, and used as T1w-reference throughout the
workflow. 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 6.0.7.12, RRID:SCR_002823,
Zhang, Brady, and Smith 2001)</span>. Brain surfaces were reconstructed
using <code>recon-all</code> <span class="citation"
data-cites="fs_reconall">(FreeSurfer 7.3.2, 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>. A
T2-weighted image was used to improve pial surface refinement. Brain
surfaces were reconstructed using <code>recon-all</code> <span
class="citation" data-cites="fs_reconall">(FreeSurfer 7.3.2,
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 one standard space
(MNI152NLin2009cAsym) was performed through nonlinear registration with
<code>antsRegistration</code> (ANTs 2.5.1), using brain-extracted
versions of both T1w reference and the T1w template. The following
template was were selected for spatial normalization and accessed with
<em>TemplateFlow</em> <span class="citation"
data-cites="templateflow">(24.2.0, Ciric et al. 2022)</span>: <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].</p>
</dd>
</dl>
<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em>
0.10.4 <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&#39;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 class="unnumbered" id="references">References</h3>
<div id="refs" class="references csl-bib-body hanging-indent"
data-entry-spacing="0" role="list">
<div id="ref-nilearn" class="csl-entry" role="listitem">
Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe
Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand
Thirion, and Gael Varoquaux. 2014. <span>“Machine Learning for
Neuroimaging with Scikit-Learn.”</span> <em>Frontiers in
Neuroinformatics</em> 8. <a
href="https://doi.org/10.3389/fninf.2014.00014">https://doi.org/10.3389/fninf.2014.00014</a>.
</div>
<div id="ref-ants" class="csl-entry" role="listitem">
Avants, B. B., C. L. Epstein, M. Grossman, and J. C. Gee. 2008.
<span>“Symmetric Diffeomorphic Image Registration with
Cross-Correlation: Evaluating Automated Labeling of Elderly and
Neurodegenerative Brain.”</span> <em>Medical Image Analysis</em> 12 (1):
26–41. <a
href="https://doi.org/10.1016/j.media.2007.06.004">https://doi.org/10.1016/j.media.2007.06.004</a>.
</div>
<div id="ref-templateflow" class="csl-entry" role="listitem">
Ciric, R., William H. Thompson, R. Lorenz, M. Goncalves, E. MacNicol, C.
J. Markiewicz, Y. O. Halchenko, et al. 2022.
<span>“<span>TemplateFlow</span>: <span>FAIR</span>-Sharing of
Multi-Scale, Multi-Species Brain Models.”</span> <em>Nature Methods</em>
19: 1568–71. <a
href="https://doi.org/10.1038/s41592-022-01681-2">https://doi.org/10.1038/s41592-022-01681-2</a>.
</div>
<div id="ref-fs_reconall" class="csl-entry" role="listitem">
Dale, Anders M., Bruce Fischl, and Martin I. Sereno. 1999.
<span>“Cortical Surface-Based Analysis: I. Segmentation and Surface
Reconstruction.”</span> <em>NeuroImage</em> 9 (2): 179–94. <a
href="https://doi.org/10.1006/nimg.1998.0395">https://doi.org/10.1006/nimg.1998.0395</a>.
</div>
<div id="ref-fmriprep2" class="csl-entry" role="listitem">
Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L.
Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018.
<span>“fMRIPrep 24.1.0.dev28+gfe7c9ff8.d20240822.”</span>
<em>Software</em>. <a
href="https://doi.org/10.5281/zenodo.852659">https://doi.org/10.5281/zenodo.852659</a>.
</div>
<div id="ref-fmriprep1" class="csl-entry" role="listitem">
Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse
Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2019.
<span>“<span class="nocase">fMRIPrep</span>: A Robust Preprocessing
Pipeline for Functional <span>MRI</span>.”</span> <em>Nature
Methods</em> 16: 111–16. <a
href="https://doi.org/10.1038/s41592-018-0235-4">https://doi.org/10.1038/s41592-018-0235-4</a>.
</div>
<div id="ref-mni152nlin2009casym" class="csl-entry" role="listitem">
Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009.
<span>“Unbiased Nonlinear Average Age-Appropriate Brain Templates from
Birth to Adulthood.”</span> <em>NeuroImage</em> 47, Supplement 1: S102.
<a
href="https://doi.org/10.1016/S1053-8119(09)70884-5">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.
</div>
<div id="ref-nipype1" class="csl-entry" role="listitem">
Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M.
L. Waskom, and S. Ghosh. 2011. <span>“Nipype: A Flexible, Lightweight
and Extensible Neuroimaging Data Processing Framework in Python.”</span>
<em>Frontiers in Neuroinformatics</em> 5: 13. <a
href="https://doi.org/10.3389/fninf.2011.00013">https://doi.org/10.3389/fninf.2011.00013</a>.
</div>
<div id="ref-nipype2" class="csl-entry" role="listitem">
Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz,
Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka,
et al. 2018. <span>“Nipype.”</span> <em>Software</em>. <a
href="https://doi.org/10.5281/zenodo.596855">https://doi.org/10.5281/zenodo.596855</a>.
</div>
<div id="ref-mindboggle" class="csl-entry" role="listitem">
Klein, Arno, Satrajit S. Ghosh, Forrest S. Bao, Joachim Giard, Yrjö
Häme, Eliezer Stavsky, Noah Lee, et al. 2017. <span>“Mindboggling
Morphometry of Human Brains.”</span> <em>PLOS Computational Biology</em>
13 (2): e1005350. <a
href="https://doi.org/10.1371/journal.pcbi.1005350">https://doi.org/10.1371/journal.pcbi.1005350</a>.
</div>
<div id="ref-n4" class="csl-entry" role="listitem">
Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A.
Yushkevich, and J. C. Gee. 2010. <span>“N4ITK: Improved N3 Bias
Correction.”</span> <em>IEEE Transactions on Medical Imaging</em> 29
(6): 1310–20. <a
href="https://doi.org/10.1109/TMI.2010.2046908">https://doi.org/10.1109/TMI.2010.2046908</a>.
</div>
<div id="ref-fsl_fast" class="csl-entry" role="listitem">
Zhang, Y., M. Brady, and S. Smith. 2001. <span>“Segmentation of Brain
<span>MR</span> Images Through a Hidden Markov Random Field Model and
the Expectation-Maximization Algorithm.”</span> <em>IEEE Transactions on
Medical Imaging</em> 20 (1): 45–57. <a
href="https://doi.org/10.1109/42.906424">https://doi.org/10.1109/42.906424</a>.
</div>
</div></div>
        </div>


        <div class="tab-pane fade " id="md-tab-pane" role="tabpanel" aria-labelledby="md-tab" tabindex="0">
            <div class="boiler-md p-3 m-4 bg-primary" style="--bs-bg-opacity: .04;"><pre>
Results included in this manuscript come from preprocessing
performed using *fMRIPrep* 24.1.0.dev28+gfe7c9ff8.d20240822
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.8.6
(@nipype1; @nipype2; RRID:SCR_002502).


Anatomical data preprocessing

: A total of 1 T1-weighted (T1w) images were found within the input
BIDS dataset. The T1w image was corrected for intensity
non-uniformity (INU) with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.5.1
[@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 6.0.7.12, RRID:SCR_002823, @fsl_fast].
Brain surfaces were reconstructed using `recon-all` [FreeSurfer 7.3.2,
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].
A T2-weighted image was used to improve pial surface refinement.
Brain surfaces were reconstructed using `recon-all` [FreeSurfer 7.3.2,
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 one standard space (MNI152NLin2009cAsym) was performed through
nonlinear registration with `antsRegistration` (ANTs 2.5.1),
using brain-extracted versions of both T1w reference and the T1w template.
The following template was were selected for spatial normalization
and accessed with *TemplateFlow* [24.2.0, @templateflow]:
*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].


Many internal operations of *fMRIPrep* use
*Nilearn* 0.10.4 [@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>


        <div class="tab-pane fade " id="tex-tab-pane" role="tabpanel" aria-labelledby="tex-tab" tabindex="0">
            <div class="boiler-tex p-3 m-4 bg-primary" style="--bs-bg-opacity: .04;"><pre>Results included in this manuscript come from preprocessing performed
using \emph{fMRIPrep} 24.1.0.dev28+gfe7c9ff8.d20240822
(\citet{fmriprep1}; \citet{fmriprep2}; RRID:SCR\_016216), which is based
on \emph{Nipype} 1.8.6 (\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 T1w image was corrected for intensity non-uniformity (INU)
with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with ANTs
2.5.1 \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 6.0.7.12, RRID:SCR\_002823,][]{fsl_fast}. Brain
surfaces were reconstructed using \texttt{recon-all} \citep[FreeSurfer
7.3.2, 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}. A T2-weighted image was used to
improve pial surface refinement. Brain surfaces were reconstructed using
\texttt{recon-all} \citep[FreeSurfer 7.3.2,
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 one standard space (MNI152NLin2009cAsym) was performed
through nonlinear registration with \texttt{antsRegistration} (ANTs
2.5.1), using brain-extracted versions of both T1w reference and the T1w
template. The following template was were selected for spatial
normalization and accessed with \emph{TemplateFlow}
\citep[24.2.0,][]{templateflow}: \emph{ICBM 152 Nonlinear Asymmetrical
template version 2009c} {[}\citet{mni152nlin2009casym},
RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]}.
\end{description}

Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.10.4
\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}.

\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.

\subsubsection{References}\label{references}

  \bibliography{/data/derivatives/bootcamp-geneva/fmriprep-24.0.0/logs/CITATION.bib}</pre>
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@article{patriat_improved_2017,
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    url = {https://www.sciencedirect.com/science/article/pii/S1053811916304360},
    doi = {10.1016/j.neuroimage.2016.08.051},
    abstract = {Head motion is a significant source of noise in the estimation of functional connectivity from resting-state functional MRI (rs-fMRI). Current strategies to reduce this noise include image realignment, censoring time points corrupted by motion, and including motion realignment parameters and their derivatives as additional nuisance regressors in the general linear model. However, this nuisance regression approach assumes that the motion-induced signal changes are linearly related to the estimated realignment parameters, which is not always the case. In this study we develop an improved model of motion-related signal changes, where nuisance regressors are formed by first rotating and translating a single brain volume according to the estimated motion, re-registering the data, and then performing a principal components analysis (PCA) on the resultant time series of both moved and re-registered data. We show that these “Motion Simulated (MotSim)” regressors account for significantly greater fraction of variance, result in higher temporal signal-to-noise, and lead to functional connectivity estimates that are less affected by motion compared to the most common current approach of using the realignment parameters and their derivatives as nuisance regressors. This improvement should lead to more accurate estimates of functional connectivity, particularly in populations where motion is prevalent, such as patients and young children.},
    urldate = {2017-01-18},
    journal = {NeuroImage},
    author = {Patriat, Rémi and Reynolds, Richard C. and Birn, Rasmus M.},
    month = jan,
    year = {2017},
    keywords = {Motion, Correction, Methods, Rs-fMRI},
    pages = {74--82},
}
</pre>
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        </div>

        </div>
        <div id="errors" class="ps-4 pe-4 mb-2">
<h2 class="sub-report-group mt-4">Errors</h2>                    <p class="alert alert-success" role="alert">No errors to report!</p>
        </div>
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