ljchang
/
Sherlock


			
							123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741742743744745746747748749750751752753754755756757758759760761762
							<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
<title></title>
<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/js/bootstrap.min.js" integrity="sha384-ChfqqxuZUCnJSK3+MXmPNIyE6ZbWh2IMqE241rYiqJxyMiZ6OW/JmZQ5stwEULTy" crossorigin="anonymous"></script>
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css" integrity="sha384-MCw98/SFnGE8fJT3GXwEOngsV7Zt27NXFoaoApmYm81iuXoPkFOJwJ8ERdknLPMO" crossorigin="anonymous">
<style type="text/css">
.sub-report-title {}
.run-title {}

h1 { padding-top: 35px; }
h2 { padding-top: 20px; }
h3 { padding-top: 15px; }

.elem-desc {}
.elem-filename {}

div.elem-image {
  width: 100%;
  page-break-before:always;
}

.elem-image object.svg-reportlet {
    width: 100%;
    padding-bottom: 5px;
}
body {
    padding: 65px 10px 10px;
}

.boiler-html {
    font-family: "Bitstream Charter", "Georgia", Times;
    margin: 20px 25px;
    padding: 10px;
    background-color: #F8F9FA;
}

div#boilerplate pre {
    margin: 20px 25px;
    padding: 10px;
    background-color: #F8F9FA;
}

</style>
</head>
<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="#Functional">Functional</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>
</div>
</nav>
<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>
<ul class="elem-desc">
		<li>Subject ID: sub-09</li>
		<li>Structural images: 1 T1-weighted </li>
		<li>Functional series: 1</li>
		<ul class="elem-desc">
			<li>Task: sherlockPart1 (1 run)</li>
		</ul>
		<li>Resampling targets: MNI152NLin2009cAsym, fsaverage5
		<li>FreeSurfer reconstruction: Not run</li>
	</ul>    </div>
    <div id="Anatomical">
    <h1 class="sub-report-title">Anatomical</h1>
<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: 192x255x170</li>
			<li>Output voxel size: 0.9mm x 0.86mm x 0.86mm</li>
			<li>Discarded images: 0</li>

		</ul><h3 class="elem-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-desc">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><br />                        <div class="elem-image">
                        <object class="svg-reportlet" type="image/svg+xml" data="./sub-09/figures/sub-09_seg_brainmask.svg">filename:sub-09/figures/sub-09_seg_brainmask.svg</object>
                        </div>
                        <div class="elem-filename">
                            Get figure file: <a href="./sub-09/figures/sub-09_seg_brainmask.svg" target="_blank">sub-09/figures/sub-09_seg_brainmask.svg</a>
                        </div>
<h3 class="elem-title">T1 to MNI registration</h3><p class="elem-desc">Nonlinear mapping of the T1w image into MNI space. Hover on the panel with the mouse to transition between both spaces.<p><br />                        <div class="elem-image">
                        <object class="svg-reportlet" type="image/svg+xml" data="./sub-09/figures/sub-09_t1_2_mni.svg">filename:sub-09/figures/sub-09_t1_2_mni.svg</object>
                        </div>
                        <div class="elem-filename">
                            Get figure file: <a href="./sub-09/figures/sub-09_t1_2_mni.svg" target="_blank">sub-09/figures/sub-09_t1_2_mni.svg</a>
                        </div>
    </div>
    <div id="Functional">
    <h1 class="sub-report-title">Functional</h1>
<h3 class="elem-title">Summary</h3>
		<ul class="elem-desc">
			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
			<li>Slice timing correction: Not applied</li>
			<li>Susceptibility distortion correction: FLB ("fieldmap-less", SyN-based)</li>
			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
			<li>Functional series resampled to spaces: template, fsaverage5</li>
			<li>Confounds collected: csf, white_matter, global_signal, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, cosine08, cosine09, cosine10, cosine11, cosine12, cosine13, cosine14, cosine15, cosine16, cosine17, cosine18, cosine19, cosine20, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z</li>
		</ul><h3 class="elem-title">Susceptibility distortion correction</h3><p class="elem-desc">Results of performing susceptibility distortion correction (SDC) on the EPI<p><br />                        <div class="elem-image">
                        <object class="svg-reportlet" type="image/svg+xml" data="./sub-09/figures/sub-09_task-sherlockPart1_sdc_syn.svg">filename:sub-09/figures/sub-09_task-sherlockPart1_sdc_syn.svg</object>
                        </div>
                        <div class="elem-filename">
                            Get figure file: <a href="./sub-09/figures/sub-09_task-sherlockPart1_sdc_syn.svg" target="_blank">sub-09/figures/sub-09_task-sherlockPart1_sdc_syn.svg</a>
                        </div>
<h3 class="elem-title">ROIs in BOLD space</h3><p class="elem-desc">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.<p><br />                        <div class="elem-image">
                        <object class="svg-reportlet" type="image/svg+xml" data="./sub-09/figures/sub-09_task-sherlockPart1_rois.svg">filename:sub-09/figures/sub-09_task-sherlockPart1_rois.svg</object>
                        </div>
                        <div class="elem-filename">
                            Get figure file: <a href="./sub-09/figures/sub-09_task-sherlockPart1_rois.svg" target="_blank">sub-09/figures/sub-09_task-sherlockPart1_rois.svg</a>
                        </div>
<h3 class="elem-title">EPI to T1 registration</h3><p class="elem-desc">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR<p><br />                        <div class="elem-image">
                        <object class="svg-reportlet" type="image/svg+xml" data="./sub-09/figures/sub-09_task-sherlockPart1_flirtbbr.svg">filename:sub-09/figures/sub-09_task-sherlockPart1_flirtbbr.svg</object>
                        </div>
                        <div class="elem-filename">
                            Get figure file: <a href="./sub-09/figures/sub-09_task-sherlockPart1_flirtbbr.svg" target="_blank">sub-09/figures/sub-09_task-sherlockPart1_flirtbbr.svg</a>
                        </div>
<h3 class="elem-title">BOLD Summary</h3><p class="elem-desc">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.<p><br />                        <div class="elem-image">
                        <object class="svg-reportlet" type="image/svg+xml" data="./sub-09/figures/sub-09_task-sherlockPart1_carpetplot.svg">filename:sub-09/figures/sub-09_task-sherlockPart1_carpetplot.svg</object>
                        </div>
                        <div class="elem-filename">
                            Get figure file: <a href="./sub-09/figures/sub-09_task-sherlockPart1_carpetplot.svg" target="_blank">sub-09/figures/sub-09_task-sherlockPart1_carpetplot.svg</a>
                        </div>
    </div>
    <div id="About">
    <h1 class="sub-report-title">About</h1>
<ul>
		<li>FMRIPrep version: 1.2.6-1</li>
		<li>FMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /work/04578/delavega/lonestar/datasets/ds001132 /work/04578/delavega/lonestar/fmriprep/out/ds001132 participant --fs-license-file /work/04578/delavega/lonestar/fmriprep/license.txt --fs-no-reconall --task sherlockPart1 --participant-label 06 07 08 09 10 --nthreads 24 --mem_mb 64000 --skip_bids_validation -w /work/04578/delavega/lonestar/fmriprep/work/ds001132 --use-syn-sdc</code></li>
		<li>Date preprocessed: 2019-08-26 17:28:37 +0000</li>
	</ul>
</div>    </div>

<div id="boilerplate">
    <h1 class="sub-report-title">Methods</h1>
    <p>We kindly ask to report results preprocessed with fMRIPrep 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>
        </li>
        <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>fMRIPprep</em> 1.2.6-1 (<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> 1.1.7 (<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>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010, ANTs 2.2.0)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using <code>antsBrainExtraction.sh</code> (ANTs 2.2.0), using OASIS as target template. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c <span class="citation" data-cites="mni">(Fonov et al. 2009, RRID:SCR_008796)</span> was performed through nonlinear registration with <code>antsRegistration</code> <span class="citation" data-cites="ants">(ANTs 2.2.0, RRID:SCR_004757, Avants et al. 2008)</span>, using brain-extracted versions of both T1w volume and 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>.</p>
</dd>
<dt>Functional data preprocessing</dt>
<dd><p>For each of the 1 BOLD 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 <em>fMRIPrep</em>. A deformation field to correct for susceptibility distortions was estimated based on <em>fMRIPrep</em>’s <em>fieldmap-less</em> approach. The deformation field is that resulting from co-registering the BOLD reference to the same-subject T1w-reference with its intensity inverted <span class="citation" data-cites="fieldmapless1 fieldmapless2">(Wang et al. 2017; Huntenburg 2014)</span>. Registration is performed with <code>antsRegistration</code> (ANTs 2.2.0), and the process regularized by constraining deformation to be nonzero only along the phase-encoding direction, and modulated with an average fieldmap template <span class="citation" data-cites="fieldmapless3">(Treiber et al. 2016)</span>. Based on the estimated susceptibility distortion, an unwarped BOLD reference was calculated for a more accurate co-registration with the anatomical reference. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. 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 <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying a single, composite transform to correct for head-motion and susceptibility distortions. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled to MNI152NLin2009cAsym standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). Six tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, six components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and template spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
</dd>
</dl>
<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.5.0 <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="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-compcor">
<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</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-mni">
<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>, Organization for human brain mapping 2009 annual meeting, 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-bbr">
<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
</div>
<div id="ref-fieldmapless2">
<p>Huntenburg, Julia M. 2014. “Evaluating Nonlinear Coregistration of BOLD EPI and T1w Images.” Master’s Thesis, Berlin: Freie Universität. <a href="http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A" class="uri">http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A</a>.</p>
</div>
<div id="ref-mcflirt">
<p>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.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
</div>
<div id="ref-flirt">
<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
</div>
<div id="ref-lanczos">
<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
</div>
<div id="ref-power_fd_dvars">
<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
</div>
<div id="ref-fieldmapless3">
<p>Treiber, Jeffrey Mark, Nathan S. White, Tyler Christian Steed, Hauke Bartsch, Dominic Holland, Nikdokht Farid, Carrie R. McDonald, Bob S. Carter, Anders Martin Dale, and Clark C. Chen. 2016. “Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images.” <em>PLOS ONE</em> 11 (3): e0152472. <a href="https://doi.org/10.1371/journal.pone.0152472" class="uri">https://doi.org/10.1371/journal.pone.0152472</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-fieldmapless1">
<p>Wang, Sijia, Daniel J. Peterson, J. C. Gatenby, Wenbin Li, Thomas J. Grabowski, and Tara M. Madhyastha. 2017. “Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI.” <em>Frontiers in Neuroinformatics</em> 11. <a href="https://doi.org/10.3389/fninf.2017.00017" class="uri">https://doi.org/10.3389/fninf.2017.00017</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 *fMRIPprep* 1.2.6-1
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.1.7
(@nipype1; @nipype2; RRID:SCR_002502).

Anatomical data preprocessing

: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
using `N4BiasFieldCorrection` [@n4, ANTs 2.2.0],
and used as T1w-reference throughout the workflow.
The T1w-reference was then skull-stripped using `antsBrainExtraction.sh`
(ANTs 2.2.0), using OASIS as target template.
Spatial normalization to the ICBM 152 Nonlinear Asymmetrical
template version 2009c [@mni, RRID:SCR_008796] was performed
through nonlinear registration with `antsRegistration`
[ANTs 2.2.0, RRID:SCR_004757, @ants], using
brain-extracted versions of both T1w volume and 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].


Functional data preprocessing

: For each of the 1 BOLD 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*.
A deformation field to correct for susceptibility distortions was estimated
based on *fMRIPrep*'s *fieldmap-less* approach.
The deformation field is that resulting from co-registering the BOLD reference
to the same-subject T1w-reference with its intensity inverted [@fieldmapless1;
@fieldmapless2].
Registration is performed with `antsRegistration` (ANTs 2.2.0), and
the process regularized by constraining deformation to be nonzero only
along the phase-encoding direction, and modulated with an average fieldmap
template [@fieldmapless3].
Based on the estimated susceptibility distortion, an
unwarped BOLD reference was calculated for a more accurate
co-registration with the anatomical reference.
The BOLD reference was then co-registered to the T1w reference using
`flirt` [FSL 5.0.9, @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.
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 5.0.9, @mcflirt].
The BOLD time-series (including slice-timing correction when applied)
were resampled onto their original, native space by applying
a single, composite transform to correct for head-motion and
susceptibility distortions.
These resampled BOLD time-series will be referred to as *preprocessed
BOLD in original space*, or just *preprocessed BOLD*.
The BOLD time-series were resampled to MNI152NLin2009cAsym standard space,
generating a *preprocessed BOLD run 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 BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
Six tCompCor components are then calculated from the top 5% variable
voxels within a mask covering the subcortical regions.
This subcortical mask is obtained by heavily eroding the brain mask,
which ensures it does not include cortical GM regions.
For aCompCor, six components are calculated within the intersection of
the aforementioned mask and the union of CSF and WM masks calculated
in T1w space, after their projection to the native space of each
functional run (using the inverse BOLD-to-T1w transformation).
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
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 template 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 *fMRIPrep* use
*Nilearn* 0.5.0 [@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").


### 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{fMRIPprep} 1.2.6-1 (\citet{fmriprep1}; \citet{fmriprep2};
RRID:SCR\_016216), which is based on \emph{Nipype} 1.1.7
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).

\begin{description}
\item[Anatomical data preprocessing]
The T1-weighted (T1w) image was corrected for intensity non-uniformity
(INU) using \texttt{N4BiasFieldCorrection} \citep[ANTs 2.2.0]{n4}, and
used as T1w-reference throughout the workflow. The T1w-reference was
then skull-stripped using \texttt{antsBrainExtraction.sh} (ANTs 2.2.0),
using OASIS as target template. Spatial normalization to the ICBM 152
Nonlinear Asymmetrical template version 2009c
\citep[RRID:SCR\_008796]{mni} was performed through nonlinear
registration with \texttt{antsRegistration} \citep[ANTs 2.2.0,
RRID:SCR\_004757,][]{ants}, using brain-extracted versions of both T1w
volume and 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}.
\item[Functional data preprocessing]
For each of the 1 BOLD 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}. A deformation field to correct for
susceptibility distortions was estimated based on \emph{fMRIPrep}'s
\emph{fieldmap-less} approach. The deformation field is that resulting
from co-registering the BOLD reference to the same-subject T1w-reference
with its intensity inverted \citep{fieldmapless1, fieldmapless2}.
Registration is performed with \texttt{antsRegistration} (ANTs 2.2.0),
and the process regularized by constraining deformation to be nonzero
only along the phase-encoding direction, and modulated with an average
fieldmap template \citep{fieldmapless3}. Based on the estimated
susceptibility distortion, an unwarped BOLD reference was calculated for
a more accurate co-registration with the anatomical reference. The BOLD
reference was then co-registered to the T1w reference using
\texttt{flirt} \citep[FSL 5.0.9,][]{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. 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 5.0.9,][]{mcflirt}. The BOLD
time-series (including slice-timing correction when applied) were
resampled onto their original, native space by applying a single,
composite transform to correct for head-motion and susceptibility
distortions. These resampled BOLD time-series will be referred to as
\emph{preprocessed BOLD in original space}, or just \emph{preprocessed
BOLD}. The BOLD time-series were resampled to MNI152NLin2009cAsym
standard space, generating a \emph{preprocessed BOLD run 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 BOLD}: framewise displacement (FD), DVARS and
three region-wise global signals. FD and DVARS are calculated for each
functional run, both using their implementations in \emph{Nipype}
\citep[following the definitions by][]{power_fd_dvars}. The three global
signals are extracted within the CSF, the WM, and the whole-brain masks.
Additionally, a set of physiological regressors were extracted to allow
for component-based noise correction \citep[\emph{CompCor},][]{compcor}.
Principal components are estimated after high-pass filtering the
\emph{preprocessed BOLD} time-series (using a discrete cosine filter
with 128s cut-off) for the two \emph{CompCor} variants: temporal
(tCompCor) and anatomical (aCompCor). Six tCompCor components are then
calculated from the top 5\% variable voxels within a mask covering the
subcortical regions. This subcortical mask is obtained by heavily
eroding the brain mask, which ensures it does not include cortical GM
regions. For aCompCor, six components are calculated within the
intersection of the aforementioned mask and the union of CSF and WM
masks calculated in T1w space, after their projection to the native
space of each functional run (using the inverse BOLD-to-T1w
transformation). The head-motion estimates calculated in the correction
step were also placed within the corresponding confounds file. 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 template 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).
\end{description}

Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.5.0
\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{references}{%
\subsubsection{References}\label{references}}

\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
<h3>Bibliography</h3>
<pre>@article{fmriprep1,
    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
    year = {2018},
    doi = {10.1038/s41592-018-0235-4},
    journal = {Nature Methods}
}

@article{fmriprep2,
    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
    title = {fMRIPrep},
    year = 2018,
    doi = {10.5281/zenodo.852659},
    publisher = {Zenodo},
    journal = {Software}
}

@article{nipype1,
    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
    doi = {10.3389/fninf.2011.00013},
    journal = {Frontiers in Neuroinformatics},
    pages = 13,
    shorttitle = {Nipype},
    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
    volume = 5,
    year = 2011
}

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

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

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


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

@article{mni,
    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
    doi = {10.1016/S1053-8119(09)70884-5},
    issn = {1053-8119},
    journal = {NeuroImage},
    pages = {S102},
    series = {Organization for Human Brain Mapping 2009 Annual Meeting},
    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
    url = {http://www.sciencedirect.com/science/article/pii/S1053811909708845},
    volume = {47, Supplement 1},
    year = 2009
}

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

@article{posse_t2s,
    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
    journal = {Magnetic Resonance in Medicine},
    number = 1,
    pages = {87-97},
    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
    volume = 42,
    year = 1999
}
</pre>
</div>
    </div>
    <p>Alternatively, an interactive <a href="http://fmriprep.readthedocs.io/en/latest/citing.html">boilerplate generator</a> is available in the <a href="https://fmriprep.org">documentation website</a>.</p>
</div>

<div id="errors">
    <h1 class="sub-report-title">Errors</h1>
    <ul>
        <li>No errors to report!</li>
    </ul>
</div>


<script type="text/javascript">
    function toggle(id) {
        var element = document.getElementById(id);
        if(element.style.display == 'block')
            element.style.display = 'none';
        else
            element.style.display = 'block';
    }
</script>
</body>
</html>