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  50. <body>
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  51. 

  52. 

  53. <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
    54. 

  54. <div class="collapse navbar-collapse">
    55. 

  55.     <ul class="navbar-nav">
    56. 

  56.         <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
    57. 

  57.         <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
    58. 

  58.         <li class="nav-item"><a class="nav-link" href="#Functional">Functional</a></li>
    59. 

  59.         <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
    60. 

  60.         <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
    61. 

  61.         <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
    62. 

  62.     </ul>
    63. 

  63. </div>
    64. 

  64. </nav>
    65. 

  65. <noscript>
    66. 

  66.     <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
    67. 

  67. </noscript>
    68. 

  68. 

  69.     <div id="Summary">
    70. 

  70.     <h1 class="sub-report-title">Summary</h1>
    71. 

  71. <ul class="elem-desc">
    72. 

  72. 		<li>Subject ID: sub-03</li>
    73. 

  73. 		<li>Structural images: 1 T1-weighted </li>
    74. 

  74. 		<li>Functional series: 1</li>
    75. 

  75. 		<ul class="elem-desc">
    76. 

  76. 			<li>Task: sherlockPart1 (1 run)</li>
    77. 

  77. 		</ul>
    78. 

  78. 		<li>Resampling targets: MNI152NLin2009cAsym, fsaverage5
    79. 

  79. 		<li>FreeSurfer reconstruction: Not run</li>
    80. 

  80. 	</ul>    </div>
    81. 

  81.     <div id="Anatomical">
    82. 

  82.     <h1 class="sub-report-title">Anatomical</h1>
    83. 

  83. <h3 class="elem-title">Anatomical Conformation</h3>
    84. 

  84. 		<ul class="elem-desc">
    85. 

  85. 			<li>Input T1w images: 1</li>
    86. 

  86. 			<li>Output orientation: RAS</li>
    87. 

  87. 			<li>Output dimensions: 192x236x223</li>
    88. 

  88. 			<li>Output voxel size: 0.9mm x 0.86mm x 0.86mm</li>
    89. 

  89. 			<li>Discarded images: 0</li>
    90. 

  90. 

  91. 		</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">
    92. 

  92.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-03/figures/sub-03_seg_brainmask.svg">filename:sub-03/figures/sub-03_seg_brainmask.svg</object>
    93. 

  93.                         </div>
    94. 

  94.                         <div class="elem-filename">
    95. 

  95.                             Get figure file: <a href="./sub-03/figures/sub-03_seg_brainmask.svg" target="_blank">sub-03/figures/sub-03_seg_brainmask.svg</a>
    96. 

  96.                         </div>
    97. 

  97. <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">
    98. 

  98.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-03/figures/sub-03_t1_2_mni.svg">filename:sub-03/figures/sub-03_t1_2_mni.svg</object>
    99. 

  99.                         </div>
    100. 

  100.                         <div class="elem-filename">
    101. 

  101.                             Get figure file: <a href="./sub-03/figures/sub-03_t1_2_mni.svg" target="_blank">sub-03/figures/sub-03_t1_2_mni.svg</a>
    102. 

  102.                         </div>
    103. 

  103.     </div>
    104. 

  104.     <div id="Functional">
    105. 

  105.     <h1 class="sub-report-title">Functional</h1>
    106. 

  106. <h3 class="elem-title">Summary</h3>
    107. 

  107. 		<ul class="elem-desc">
    108. 

  108. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    109. 

  109. 			<li>Slice timing correction: Not applied</li>
    110. 

  110. 			<li>Susceptibility distortion correction: FLB ("fieldmap-less", SyN-based)</li>
    111. 

  111. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    112. 

  112. 			<li>Functional series resampled to spaces: template, fsaverage5</li>
    113. 

  113. 			<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>
    114. 

  114. 		</ul><h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    115. 

  115. 			<p class="elem-desc">The qform has been copied from sform.</p><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">
    116. 

  116.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-03/figures/sub-03_task-sherlockPart1_sdc_syn.svg">filename:sub-03/figures/sub-03_task-sherlockPart1_sdc_syn.svg</object>
    117. 

  117.                         </div>
    118. 

  118.                         <div class="elem-filename">
    119. 

  119.                             Get figure file: <a href="./sub-03/figures/sub-03_task-sherlockPart1_sdc_syn.svg" target="_blank">sub-03/figures/sub-03_task-sherlockPart1_sdc_syn.svg</a>
    120. 

  120.                         </div>
    121. 

  121. <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">
    122. 

  122.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-03/figures/sub-03_task-sherlockPart1_rois.svg">filename:sub-03/figures/sub-03_task-sherlockPart1_rois.svg</object>
    123. 

  123.                         </div>
    124. 

  124.                         <div class="elem-filename">
    125. 

  125.                             Get figure file: <a href="./sub-03/figures/sub-03_task-sherlockPart1_rois.svg" target="_blank">sub-03/figures/sub-03_task-sherlockPart1_rois.svg</a>
    126. 

  126.                         </div>
    127. 

  127. <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">
    128. 

  128.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-03/figures/sub-03_task-sherlockPart1_flirtbbr.svg">filename:sub-03/figures/sub-03_task-sherlockPart1_flirtbbr.svg</object>
    129. 

  129.                         </div>
    130. 

  130.                         <div class="elem-filename">
    131. 

  131.                             Get figure file: <a href="./sub-03/figures/sub-03_task-sherlockPart1_flirtbbr.svg" target="_blank">sub-03/figures/sub-03_task-sherlockPart1_flirtbbr.svg</a>
    132. 

  132.                         </div>
    133. 

  133. <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">
    134. 

  134.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-03/figures/sub-03_task-sherlockPart1_carpetplot.svg">filename:sub-03/figures/sub-03_task-sherlockPart1_carpetplot.svg</object>
    135. 

  135.                         </div>
    136. 

  136.                         <div class="elem-filename">
    137. 

  137.                             Get figure file: <a href="./sub-03/figures/sub-03_task-sherlockPart1_carpetplot.svg" target="_blank">sub-03/figures/sub-03_task-sherlockPart1_carpetplot.svg</a>
    138. 

  138.                         </div>
    139. 

  139.     </div>
    140. 

  140.     <div id="About">
    141. 

  141.     <h1 class="sub-report-title">About</h1>
    142. 

  142. <ul>
    143. 

  143. 		<li>FMRIPrep version: 1.2.6-1</li>
    144. 

  144. 		<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 01 02 03 04 05 --nthreads 24 --mem_mb 64000 --skip_bids_validation -w /work/04578/delavega/lonestar/fmriprep/work/ds001132 --use-syn-sdc</code></li>
    145. 

  145. 		<li>Date preprocessed: 2019-08-26 17:28:32 +0000</li>
    146. 

  146. 	</ul>
    147. 

  147. </div>    </div>
    148. 

  148. 

  149. <div id="boilerplate">
    150. 

  150.     <h1 class="sub-report-title">Methods</h1>
    151. 

  151.     <p>We kindly ask to report results preprocessed with fMRIPrep using the following
    152. 

  152.        boilerplate</p>
    153. 

  153.     <ul class="nav nav-tabs" id="myTab" role="tablist">
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    165. 

  165.       <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>
    166. 

  166. <dl>
    167. 

  167. <dt>Anatomical data preprocessing</dt>
    168. 

  168. <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>
    169. 

  169. </dd>
    170. 

  170. <dt>Functional data preprocessing</dt>
    171. 

  171. <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>
    172. 

  172. </dd>
    173. 

  173. </dl>
    174. 

  174. <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>
    175. 

  175. <h3 id="references" class="unnumbered">References</h3>
    176. 

  176. <div id="refs" class="references">
    177. 

  177. <div id="ref-nilearn">
    178. 

  178. <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>
    179. 

  179. </div>
    180. 

  180. <div id="ref-ants">
    181. 

  181. <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>
    182. 

  182. </div>
    183. 

  183. <div id="ref-compcor">
    184. 

  184. <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>
    185. 

  185. </div>
    186. 

  186. <div id="ref-fmriprep2">
    187. 

  187. <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>
    188. 

  188. </div>
    189. 

  189. <div id="ref-fmriprep1">
    190. 

  190. <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>
    191. 

  191. </div>
    192. 

  192. <div id="ref-mni">
    193. 

  193. <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>
    194. 

  194. </div>
    195. 

  195. <div id="ref-nipype1">
    196. 

  196. <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>
    197. 

  197. </div>
    198. 

  198. <div id="ref-nipype2">
    199. 

  199. <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>
    200. 

  200. </div>
    201. 

  201. <div id="ref-bbr">
    202. 

  202. <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>
    203. 

  203. </div>
    204. 

  204. <div id="ref-fieldmapless2">
    205. 

  205. <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>
    206. 

  206. </div>
    207. 

  207. <div id="ref-mcflirt">
    208. 

  208. <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>
    209. 

  209. </div>
    210. 

  210. <div id="ref-flirt">
    211. 

  211. <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>
    212. 

  212. </div>
    213. 

  213. <div id="ref-lanczos">
    214. 

  214. <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>
    215. 

  215. </div>
    216. 

  216. <div id="ref-power_fd_dvars">
    217. 

  217. <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>
    218. 

  218. </div>
    219. 

  219. <div id="ref-fieldmapless3">
    220. 

  220. <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>
    221. 

  221. </div>
    222. 

  222. <div id="ref-n4">
    223. 

  223. <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>
    224. 

  224. </div>
    225. 

  225. <div id="ref-fieldmapless1">
    226. 

  226. <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>
    227. 

  227. </div>
    228. 

  228. <div id="ref-fsl_fast">
    229. 

  229. <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>
    230. 

  230. </div>
    231. 

  231. </div></div></div>
    232. 

  232.       <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
    233. 

  233. Results included in this manuscript come from preprocessing
    234. 

  234. performed using *fMRIPprep* 1.2.6-1
    235. 

  235. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    236. 

  236. which is based on *Nipype* 1.1.7
    237. 

  237. (@nipype1; @nipype2; RRID:SCR_002502).
    238. 

  238. 

  239. Anatomical data preprocessing
    240. 

  240. 

  241. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
    242. 

  242. using `N4BiasFieldCorrection` [@n4, ANTs 2.2.0],
    243. 

  243. and used as T1w-reference throughout the workflow.
    244. 

  244. The T1w-reference was then skull-stripped using `antsBrainExtraction.sh`
    245. 

  245. (ANTs 2.2.0), using OASIS as target template.
    246. 

  246. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical
    247. 

  247. template version 2009c [@mni, RRID:SCR_008796] was performed
    248. 

  248. through nonlinear registration with `antsRegistration`
    249. 

  249. [ANTs 2.2.0, RRID:SCR_004757, @ants], using
    250. 

  250. brain-extracted versions of both T1w volume and template.
    251. 

  251. Brain tissue segmentation of cerebrospinal fluid (CSF),
    252. 

  252. white-matter (WM) and gray-matter (GM) was performed on
    253. 

  253. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
    254. 

  254. @fsl_fast].
    255. 

  255. 

  256. 

  257. Functional data preprocessing
    258. 

  258. 

  259. : For each of the 1 BOLD runs found per subject (across all
    260. 

  260. tasks and sessions), the following preprocessing was performed.
    261. 

  261. First, a reference volume and its skull-stripped version were generated
    262. 

  262. using a custom methodology of *fMRIPrep*.
    263. 

  263. A deformation field to correct for susceptibility distortions was estimated
    264. 

  264. based on *fMRIPrep*'s *fieldmap-less* approach.
    265. 

  265. The deformation field is that resulting from co-registering the BOLD reference
    266. 

  266. to the same-subject T1w-reference with its intensity inverted [@fieldmapless1;
    267. 

  267. @fieldmapless2].
    268. 

  268. Registration is performed with `antsRegistration` (ANTs 2.2.0), and
    269. 

  269. the process regularized by constraining deformation to be nonzero only
    270. 

  270. along the phase-encoding direction, and modulated with an average fieldmap
    271. 

  271. template [@fieldmapless3].
    272. 

  272. Based on the estimated susceptibility distortion, an
    273. 

  273. unwarped BOLD reference was calculated for a more accurate
    274. 

  274. co-registration with the anatomical reference.
    275. 

  275. The BOLD reference was then co-registered to the T1w reference using
    276. 

  276. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
    277. 

  277. cost-function.
    278. 

  278. Co-registration was configured with nine degrees of freedom to account
    279. 

  279. for distortions remaining in the BOLD reference.
    280. 

  280. Head-motion parameters with respect to the BOLD reference
    281. 

  281. (transformation matrices, and six corresponding rotation and translation
    282. 

  282. parameters) are estimated before any spatiotemporal filtering using
    283. 

  283. `mcflirt` [FSL 5.0.9, @mcflirt].
    284. 

  284. The BOLD time-series (including slice-timing correction when applied)
    285. 

  285. were resampled onto their original, native space by applying
    286. 

  286. a single, composite transform to correct for head-motion and
    287. 

  287. susceptibility distortions.
    288. 

  288. These resampled BOLD time-series will be referred to as *preprocessed
    289. 

  289. BOLD in original space*, or just *preprocessed BOLD*.
    290. 

  290. The BOLD time-series were resampled to MNI152NLin2009cAsym standard space,
    291. 

  291. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
    292. 

  292. First, a reference volume and its skull-stripped version were generated
    293. 

  293. using a custom methodology of *fMRIPrep*.
    294. 

  294. Several confounding time-series were calculated based on the
    295. 

  295. *preprocessed BOLD*: framewise displacement (FD), DVARS and
    296. 

  296. three region-wise global signals.
    297. 

  297. FD and DVARS are calculated for each functional run, both using their
    298. 

  298. implementations in *Nipype* [following the definitions by @power_fd_dvars].
    299. 

  299. The three global signals are extracted within the CSF, the WM, and
    300. 

  300. the whole-brain masks.
    301. 

  301. Additionally, a set of physiological regressors were extracted to
    302. 

  302. allow for component-based noise correction [*CompCor*, @compcor].
    303. 

  303. Principal components are estimated after high-pass filtering the
    304. 

  304. *preprocessed BOLD* time-series (using a discrete cosine filter with
    305. 

  305. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    306. 

  306. and anatomical (aCompCor).
    307. 

  307. Six tCompCor components are then calculated from the top 5% variable
    308. 

  308. voxels within a mask covering the subcortical regions.
    309. 

  309. This subcortical mask is obtained by heavily eroding the brain mask,
    310. 

  310. which ensures it does not include cortical GM regions.
    311. 

  311. For aCompCor, six components are calculated within the intersection of
    312. 

  312. the aforementioned mask and the union of CSF and WM masks calculated
    313. 

  313. in T1w space, after their projection to the native space of each
    314. 

  314. functional run (using the inverse BOLD-to-T1w transformation).
    315. 

  315. The head-motion estimates calculated in the correction step were also
    316. 

  316. placed within the corresponding confounds file.
    317. 

  317. All resamplings can be performed with *a single interpolation
    318. 

  318. step* by composing all the pertinent transformations (i.e. head-motion
    319. 

  319. transform matrices, susceptibility distortion correction when available,
    320. 

  320. and co-registrations to anatomical and template spaces).
    321. 

  321. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
    322. 

  322. configured with Lanczos interpolation to minimize the smoothing
    323. 

  323. effects of other kernels [@lanczos].
    324. 

  324. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
    325. 

  325. (FreeSurfer).
    326. 

  326. 

  327. 

  328. Many internal operations of *fMRIPrep* use
    329. 

  329. *Nilearn* 0.5.0 [@nilearn, RRID:SCR_001362],
    330. 

  330. mostly within the functional processing workflow.
    331. 

  331. For more details of the pipeline, see [the section corresponding
    332. 

  332. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
    333. 

  333. 

  334. 

  335. ### References
    336. 

  336. 

  337. </pre>
    338. 

  338. </div>
    339. 

  339.       <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
    340. 

  340. using \emph{fMRIPprep} 1.2.6-1 (\citet{fmriprep1}; \citet{fmriprep2};
    341. 

  341. RRID:SCR\_016216), which is based on \emph{Nipype} 1.1.7
    342. 

  342. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
    343. 

  343. 

  344. \begin{description}
    345. 

  345. \item[Anatomical data preprocessing]
    346. 

  346. The T1-weighted (T1w) image was corrected for intensity non-uniformity
    347. 

  347. (INU) using \texttt{N4BiasFieldCorrection} \citep[ANTs 2.2.0]{n4}, and
    348. 

  348. used as T1w-reference throughout the workflow. The T1w-reference was
    349. 

  349. then skull-stripped using \texttt{antsBrainExtraction.sh} (ANTs 2.2.0),
    350. 

  350. using OASIS as target template. Spatial normalization to the ICBM 152
    351. 

  351. Nonlinear Asymmetrical template version 2009c
    352. 

  352. \citep[RRID:SCR\_008796]{mni} was performed through nonlinear
    353. 

  353. registration with \texttt{antsRegistration} \citep[ANTs 2.2.0,
    354. 

  354. RRID:SCR\_004757,][]{ants}, using brain-extracted versions of both T1w
    355. 

  355. volume and template. Brain tissue segmentation of cerebrospinal fluid
    356. 

  356. (CSF), white-matter (WM) and gray-matter (GM) was performed on the
    357. 

  357. brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
    358. 

  358. RRID:SCR\_002823,][]{fsl_fast}.
    359. 

  359. \item[Functional data preprocessing]
    360. 

  360. For each of the 1 BOLD runs found per subject (across all tasks and
    361. 

  361. sessions), the following preprocessing was performed. First, a reference
    362. 

  362. volume and its skull-stripped version were generated using a custom
    363. 

  363. methodology of \emph{fMRIPrep}. A deformation field to correct for
    364. 

  364. susceptibility distortions was estimated based on \emph{fMRIPrep}'s
    365. 

  365. \emph{fieldmap-less} approach. The deformation field is that resulting
    366. 

  366. from co-registering the BOLD reference to the same-subject T1w-reference
    367. 

  367. with its intensity inverted \citep{fieldmapless1, fieldmapless2}.
    368. 

  368. Registration is performed with \texttt{antsRegistration} (ANTs 2.2.0),
    369. 

  369. and the process regularized by constraining deformation to be nonzero
    370. 

  370. only along the phase-encoding direction, and modulated with an average
    371. 

  371. fieldmap template \citep{fieldmapless3}. Based on the estimated
    372. 

  372. susceptibility distortion, an unwarped BOLD reference was calculated for
    373. 

  373. a more accurate co-registration with the anatomical reference. The BOLD
    374. 

  374. reference was then co-registered to the T1w reference using
    375. 

  375. \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the boundary-based
    376. 

  376. registration \citep{bbr} cost-function. Co-registration was configured
    377. 

  377. with nine degrees of freedom to account for distortions remaining in the
    378. 

  378. BOLD reference. Head-motion parameters with respect to the BOLD
    379. 

  379. reference (transformation matrices, and six corresponding rotation and
    380. 

  380. translation parameters) are estimated before any spatiotemporal
    381. 

  381. filtering using \texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. The BOLD
    382. 

  382. time-series (including slice-timing correction when applied) were
    383. 

  383. resampled onto their original, native space by applying a single,
    384. 

  384. composite transform to correct for head-motion and susceptibility
    385. 

  385. distortions. These resampled BOLD time-series will be referred to as
    386. 

  386. \emph{preprocessed BOLD in original space}, or just \emph{preprocessed
    387. 

  387. BOLD}. The BOLD time-series were resampled to MNI152NLin2009cAsym
    388. 

  388. standard space, generating a \emph{preprocessed BOLD run in
    389. 

  389. MNI152NLin2009cAsym space}. First, a reference volume and its
    390. 

  390. skull-stripped version were generated using a custom methodology of
    391. 

  391. \emph{fMRIPrep}. Several confounding time-series were calculated based
    392. 

  392. on the \emph{preprocessed BOLD}: framewise displacement (FD), DVARS and
    393. 

  393. three region-wise global signals. FD and DVARS are calculated for each
    394. 

  394. functional run, both using their implementations in \emph{Nipype}
    395. 

  395. \citep[following the definitions by][]{power_fd_dvars}. The three global
    396. 

  396. signals are extracted within the CSF, the WM, and the whole-brain masks.
    397. 

  397. Additionally, a set of physiological regressors were extracted to allow
    398. 

  398. for component-based noise correction \citep[\emph{CompCor},][]{compcor}.
    399. 

  399. Principal components are estimated after high-pass filtering the
    400. 

  400. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
    401. 

  401. with 128s cut-off) for the two \emph{CompCor} variants: temporal
    402. 

  402. (tCompCor) and anatomical (aCompCor). Six tCompCor components are then
    403. 

  403. calculated from the top 5\% variable voxels within a mask covering the
    404. 

  404. subcortical regions. This subcortical mask is obtained by heavily
    405. 

  405. eroding the brain mask, which ensures it does not include cortical GM
    406. 

  406. regions. For aCompCor, six components are calculated within the
    407. 

  407. intersection of the aforementioned mask and the union of CSF and WM
    408. 

  408. masks calculated in T1w space, after their projection to the native
    409. 

  409. space of each functional run (using the inverse BOLD-to-T1w
    410. 

  410. transformation). The head-motion estimates calculated in the correction
    411. 

  411. step were also placed within the corresponding confounds file. All
    412. 

  412. resamplings can be performed with \emph{a single interpolation step} by
    413. 

  413. composing all the pertinent transformations (i.e.~head-motion transform
    414. 

  414. matrices, susceptibility distortion correction when available, and
    415. 

  415. co-registrations to anatomical and template spaces). Gridded
    416. 

  416. (volumetric) resamplings were performed using
    417. 

  417. \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
    418. 

  418. interpolation to minimize the smoothing effects of other kernels
    419. 

  419. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
    420. 

  420. \texttt{mri\_vol2surf} (FreeSurfer).
    421. 

  421. \end{description}
    422. 

  422. 

  423. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.5.0
    424. 

  424. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
    425. 

  425. processing workflow. For more details of the pipeline, see
    426. 

  426. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
    427. 

  427. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
    428. 

  428. 

  429. \hypertarget{references}{%
    430. 

  430. \subsubsection{References}\label{references}}
    431. 

  431. 

  432. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
    433. 

  433. <h3>Bibliography</h3>
    434. 

  434. <pre>@article{fmriprep1,
    435. 

  435.     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},
    436. 

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  741.     </div>
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  742.     <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>
    743. 

  743. </div>
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  744. 

  745. <div id="errors">
    746. 

  746.     <h1 class="sub-report-title">Errors</h1>
    747. 

  747.     <ul>
    748. 

  748.         <li>No errors to report!</li>
    749. 

  749.     </ul>
    750. 

  750. </div>
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  751. 

  752. 

  753. <script type="text/javascript">
    754. 

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    764.