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<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>
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#errors div, #errors p {
padding-left: 1em;
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</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 dropdown">
<a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
<div class="dropdown-menu" aria-labelledby="navbarFunctional">
<a class="dropdown-item" href="#datatype-figures_desc-summary_session-001_suffix-bold_task-wedges">Reports for: session <span class="bids-entity">001</span>, task <span class="bids-entity">wedges</span>.</a>
</div>
</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>
<div id="datatype-figures_desc-summary_suffix-T1w">
<ul class="elem-desc">
<li>Subject ID: 06</li>
<li>Structural images: 1 T1-weighted </li>
<li>Functional series: 1</li>
<ul class="elem-desc">
<li>Task: wedges (1 run)</li>
</ul>
<li>Standard output spaces: MNI152NLin2009cAsym, fsaverage, MNI152NLin6Asym</li>
<li>Non-standard output spaces: T1w</li>
<li>FreeSurfer reconstruction: Pre-existing directory</li>
</ul>
</div>
</div>
<div id="Anatomical">
<h1 class="sub-report-title">Anatomical</h1>
<div id="datatype-figures_desc-conform_extension-['.html']_suffix-T1w">
<h4 class="elem-title">A previously computed T1w template was provided.</h4>
</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-06/figures/sub-06_desc-preproc_dseg.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-06/figures/sub-06_desc-preproc_dseg.svg" target="_blank">sub-06/figures/sub-06_desc-preproc_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-06/figures/sub-06_space-MNI152NLin6Asym_desc-preproc_T1w.svg">
Problem loading figure sub-06/figures/sub-06_space-MNI152NLin6Asym_desc-preproc_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-06/figures/sub-06_space-MNI152NLin6Asym_desc-preproc_T1w.svg" target="_blank">sub-06/figures/sub-06_space-MNI152NLin6Asym_desc-preproc_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-06/figures/sub-06_space-MNI152NLin2009cAsym_desc-preproc_T1w.svg">
Problem loading figure sub-06/figures/sub-06_space-MNI152NLin2009cAsym_desc-preproc_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-06/figures/sub-06_space-MNI152NLin2009cAsym_desc-preproc_T1w.svg" target="_blank">sub-06/figures/sub-06_space-MNI152NLin2009cAsym_desc-preproc_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-06/figures/sub-06_desc-reconall_T1w.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-06/figures/sub-06_desc-reconall_T1w.svg" target="_blank">sub-06/figures/sub-06_desc-reconall_T1w.svg</a>
</div>
</div>
</div>
<div id="Functional">
<h1 class="sub-report-title">Functional</h1>
<div id="datatype-figures_desc-summary_session-001_suffix-bold_task-wedges">
<h2 class="sub-report-group">Reports for: session <span class="bids-entity">001</span>, task <span class="bids-entity">wedges</span>.</h2> <details open>
<summary>Summary</summary>
<ul class="elem-desc">
<li>Repetition time (TR): 1.49s</li>
<li>Phase-encoding (PE) direction: Anterior-Posterior</li>
<li>Single-echo EPI sequence.</li>
<li>Slice timing correction: Not applied</li>
<li>Susceptibility distortion correction: PEB/PEPOLAR (phase-encoding based / PE-POLARity)</li>
<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
<li>Non-steady-state volumes: 0</li>
</ul>
</details>
<details>
<summary>Confounds collected</summary><br />
<p>global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, csf_wm, tcompcor, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17, motion_outlier18, motion_outlier19, motion_outlier20, motion_outlier21, motion_outlier22, motion_outlier23, motion_outlier24, motion_outlier25, motion_outlier26, motion_outlier27, motion_outlier28, motion_outlier29, motion_outlier30, motion_outlier31, motion_outlier32, motion_outlier33, motion_outlier34, motion_outlier35, motion_outlier36, motion_outlier37, motion_outlier38, motion_outlier39, motion_outlier40, motion_outlier41, motion_outlier42, motion_outlier43, motion_outlier44, motion_outlier45, motion_outlier46, motion_outlier47, motion_outlier48, motion_outlier49, motion_outlier50.</p>
</details>
</div>
<div id="datatype-figures_desc-validation_session-001_suffix-bold_task-wedges">
<h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
<p class="elem-desc">
The qform has been copied from sform.
The difference in angle is 1e-05 radians.
The difference in translation is 7.9e-15mm.
</p>
</div>
<div id="datatype-figures_desc-sdc_session-001_suffix-bold_task-wedges">
<h3 class="run-title">Susceptibility distortion correction</h3><p class="elem-caption">Results of performing susceptibility distortion correction (SDC) on the EPI</p> <object class="svg-reportlet" type="image/svg+xml" data="./sub-06/figures/sub-06_ses-001_task-wedges_desc-sdc_part-mag_bold.svg">
Problem loading figure sub-06/figures/sub-06_ses-001_task-wedges_desc-sdc_part-mag_bold.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-06/figures/sub-06_ses-001_task-wedges_desc-sdc_part-mag_bold.svg" target="_blank">sub-06/figures/sub-06_ses-001_task-wedges_desc-sdc_part-mag_bold.svg</a>
</div>
</div>
<div id="datatype-figures_desc-bbregister_session-001_suffix-bold_task-wedges">
<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p> <object class="svg-reportlet" type="image/svg+xml" data="./sub-06/figures/sub-06_ses-001_task-wedges_desc-bbregister_part-mag_bold.svg">
Problem loading figure sub-06/figures/sub-06_ses-001_task-wedges_desc-bbregister_part-mag_bold.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-06/figures/sub-06_ses-001_task-wedges_desc-bbregister_part-mag_bold.svg" target="_blank">sub-06/figures/sub-06_ses-001_task-wedges_desc-bbregister_part-mag_bold.svg</a>
</div>
</div>
<div id="datatype-figures_desc-rois_session-001_suffix-bold_task-wedges">
<h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-001_task-wedges_desc-rois_part-mag_bold.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-06/figures/sub-06_ses-001_task-wedges_desc-rois_part-mag_bold.svg" target="_blank">sub-06/figures/sub-06_ses-001_task-wedges_desc-rois_part-mag_bold.svg</a>
</div>
</div>
<div id="datatype-figures_desc-compcorvar_session-001_suffix-bold_task-wedges">
<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-001_task-wedges_desc-compcorvar_part-mag_bold.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-06/figures/sub-06_ses-001_task-wedges_desc-compcorvar_part-mag_bold.svg" target="_blank">sub-06/figures/sub-06_ses-001_task-wedges_desc-compcorvar_part-mag_bold.svg</a>
</div>
</div>
<div id="datatype-figures_desc-carpetplot_session-001_suffix-bold_task-wedges">
<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-001_task-wedges_desc-carpetplot_part-mag_bold.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-06/figures/sub-06_ses-001_task-wedges_desc-carpetplot_part-mag_bold.svg" target="_blank">sub-06/figures/sub-06_ses-001_task-wedges_desc-carpetplot_part-mag_bold.svg</a>
</div>
</div>
<div id="datatype-figures_desc-confoundcorr_session-001_suffix-bold_task-wedges">
<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
Right: magnitude of the correlation between each confound time series and the
mean global signal. Strong correlations might be indicative of partial volume
effects and can inform decisions about feature orthogonalization prior to
confound regression.
</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-001_task-wedges_desc-confoundcorr_part-mag_bold.svg" style="width: 100%" />
</div>
<div class="elem-filename">
Get figure file: <a href="./sub-06/figures/sub-06_ses-001_task-wedges_desc-confoundcorr_part-mag_bold.svg" target="_blank">sub-06/figures/sub-06_ses-001_task-wedges_desc-confoundcorr_part-mag_bold.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: 20.2.3</li>
<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep -w ./workdir --participant-label 06 --anat-derivatives ./sourcedata/smriprep --fs-subjects-dir ./sourcedata/smriprep/sourcedata/freesurfer --bids-filter-file code/fmriprep_study-retinotopy_sub-06_ses-001_bids_filters.json --output-layout bids --ignore slicetiming --use-syn-sdc --output-spaces MNI152NLin2009cAsym T1w:res-iso2mm --cifti-output 91k --notrack --write-graph --skip_bids_validation --omp-nthreads 8 --nprocs 16 --mem_mb 65536 --fs-license-file code/freesurfer.license --resource-monitor sourcedata/retinotopy ./ participant</code></li>
<li>Date preprocessed: 2021-10-05 01:51:08 +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>
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<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> 20.2.3 (<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> 1.6.1 (<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 0 T1-weighted (T1w) images were found within the input BIDS dataset. Anatomical preprocessing was reused from previously existing derivative objects.</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 by aligning and averaging 1 single-band references (SBRefs). A B0-nonuniformity map (or <em>fieldmap</em>) was estimated based on two (or more) echo-planar imaging (EPI) references with opposing phase-encoding directions, with <code>3dQwarp</code> <span class="citation" data-cites="afni">Cox and Hyde (1997)</span> (AFNI 20160207). Based on the estimated susceptibility distortion, a corrected EPI (echo-planar imaging) 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>bbregister</code> (FreeSurfer) which implements boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span>. Co-registration was configured with six degrees of freedom. 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>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. The BOLD time-series were resampled onto the following surfaces (FreeSurfer reconstruction nomenclature): <em>fsaverage</em>. 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 into 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>. <em>Grayordinates</em> files <span class="citation" data-cites="hcppipelines">(Glasser et al. 2013)</span> containing 91k samples were also generated using the highest-resolution <code>fsaverage</code> as intermediate standardized surface space. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD was computed using two formulations following Power (absolute sum of relative motions, <span class="citation" data-cites="power_fd_dvars">Power et al. (2014)</span>) and Jenkinson (relative root mean square displacement between affines, <span class="citation" data-cites="mcflirt">Jenkinson et al. (2002)</span>). 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). tCompCor components are then calculated from the top 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are subtracted a mask of pixels that likely contain a volume fraction of GM. This mask is obtained by dilating a GM mask extracted from the FreeSurfer’s <em>aseg</em> segmentation, and it ensures components are not extracted from voxels containing a minimal fraction of GM. Finally, these masks are resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. 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 output 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> 0.6.2 <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-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-afni">
<p>Cox, Robert W., and James S. Hyde. 1997. “Software Tools for Analysis and Visualization of fMRI Data.” <em>NMR in Biomedicine</em> 10 (4-5): 171–78. <a href="https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L" class="uri">https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L</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-hcppipelines">
<p>Glasser, Matthew F., Stamatios N. Sotiropoulos, J. Anthony Wilson, Timothy S. Coalson, Bruce Fischl, Jesper L. Andersson, Junqian Xu, et al. 2013. “The Minimal Preprocessing Pipelines for the Human Connectome Project.” <em>NeuroImage</em>, Mapping the connectome, 80: 105–24. <a href="https://doi.org/10.1016/j.neuroimage.2013.04.127" class="uri">https://doi.org/10.1016/j.neuroimage.2013.04.127</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-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-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-confounds_satterthwaite_2013">
<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</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* 20.2.3
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.6.1
(@nipype1; @nipype2; RRID:SCR_002502).
Anatomical data preprocessing
: A total of 0 T1-weighted (T1w) images were found within the input
BIDS dataset.
Anatomical preprocessing was reused from previously existing derivative objects.
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
by aligning and averaging
1 single-band references (SBRefs).
A B0-nonuniformity map (or *fieldmap*) was estimated based on two (or more)
echo-planar imaging (EPI) references with opposing phase-encoding
directions, with `3dQwarp` @afni (AFNI 20160207).
Based on the estimated susceptibility distortion, a corrected
EPI (echo-planar imaging) 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
`bbregister` (FreeSurfer) which implements boundary-based registration [@bbr].
Co-registration was configured with six degrees of freedom.
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].
First, a reference volume and its skull-stripped version were generated
using a custom
methodology of *fMRIPrep*.
The BOLD time-series were resampled onto the following surfaces
(FreeSurfer reconstruction nomenclature):
*fsaverage*.
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 into 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*.
*Grayordinates* files [@hcppipelines] containing 91k samples were also
generated using the highest-resolution ``fsaverage`` as intermediate standardized
surface space.
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
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).
tCompCor components are then calculated from the top 2% variable
voxels within the brain mask.
For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
are generated in anatomical space.
The implementation differs from that of Behzadi et al. in that instead
of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by dilating a GM mask extracted from the FreeSurfer's *aseg* segmentation, and it ensures components are not extracted
from voxels containing a minimal fraction of GM.
Finally, these masks are resampled into BOLD space and binarized by
thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of 0.5 mm FD or
1.5 standardised DVARS were annotated as motion outliers.
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).
Many internal operations of *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} 20.2.3 (\citet{fmriprep1}; \citet{fmriprep2};
RRID:SCR\_016216), which is based on \emph{Nipype} 1.6.1
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
\begin{description}
\item[Anatomical data preprocessing]
A total of 0 T1-weighted (T1w) images were found within the input BIDS
dataset. Anatomical preprocessing was reused from previously existing
derivative objects.
\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 by aligning and
averaging 1 single-band references (SBRefs). A B0-nonuniformity map (or
\emph{fieldmap}) was estimated based on two (or more) echo-planar
imaging (EPI) references with opposing phase-encoding directions, with
\texttt{3dQwarp} \citet{afni} (AFNI 20160207). Based on the estimated
susceptibility distortion, a corrected EPI (echo-planar imaging)
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{bbregister} (FreeSurfer) which implements
boundary-based registration \citep{bbr}. Co-registration was configured
with six degrees of freedom. 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}. First, a
reference volume and its skull-stripped version were generated using a
custom methodology of \emph{fMRIPrep}. The BOLD time-series were
resampled onto the following surfaces (FreeSurfer reconstruction
nomenclature): \emph{fsaverage}. 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 into 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}. \emph{Grayordinates} files \citep{hcppipelines}
containing 91k samples were also generated using the highest-resolution
\texttt{fsaverage} as intermediate standardized surface space. Several
confounding time-series were calculated based on the \emph{preprocessed
BOLD}: framewise displacement (FD), DVARS and three region-wise global
signals. FD was computed using two formulations following Power
(absolute sum of relative motions, \citet{power_fd_dvars}) and Jenkinson
(relative root mean square displacement between affines,
\citet{mcflirt}). 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). tCompCor components are then
calculated from the top 2\% variable voxels within the brain mask. For
aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are
generated in anatomical space. The implementation differs from that of
Behzadi et al.~in that instead of eroding the masks by 2 pixels on BOLD
space, the aCompCor masks are subtracted a mask of pixels that likely
contain a volume fraction of GM. This mask is obtained by dilating a GM
mask extracted from the FreeSurfer's \emph{aseg} segmentation, and it
ensures components are not extracted from voxels containing a minimal
fraction of GM. Finally, these masks are resampled into BOLD space and
binarized by thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the \emph{k} components with the largest
singular values are retained, such that the retained components' time
series are sufficient to explain 50 percent of variance across the
nuisance mask (CSF, WM, combined, or temporal). The remaining components
are dropped from consideration. The head-motion estimates calculated in
the correction step were also placed within the corresponding confounds
file. The confound time series derived from head motion estimates and
global signals were expanded with the inclusion of temporal derivatives
and quadratic terms for each \citep{confounds_satterthwaite_2013}.
Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
were annotated as motion outliers. All resamplings can be performed with
\emph{a single interpolation step} by composing all the pertinent
transformations (i.e.~head-motion transform matrices, susceptibility
distortion correction when available, and co-registrations to anatomical
and output spaces). Gridded (volumetric) resamplings were performed
using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
interpolation to minimize the smoothing effects of other kernels
\citep{lanczos}. Non-gridded (surface) resamplings were performed using
\texttt{mri\_vol2surf} (FreeSurfer).
\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>
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